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Haze reduction from the visible bands of LANDSAT TM and ETM+ images over a shallow water reef environment
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Remote Sensing of Environment 112 (2008) 1773 – 1783 www.elsevier.com/locate/rse
Haze reduction from the visible bands of LANDSAT TM and ETM+ images over a shallow water reef environment C.Y. Ji Department of Geodetic Engineering, University of the Philippines Diliman, Quezon City 1101, Metro Manila, Philippines Received 18 July 2006; received in revised form 31 August 2007; accepted 1 September 2007
Abstract A method for haze reduction in the visible bands of Landsat TM and ETM+ images over a shallow water marine environment is presented in this paper. This method uses the near infrared (NIR) band to estimate the spatial distribution of haze intensity in each visible band through a linear regression model established over deep water areas. As a first order approximation, the signal received at the sensor is assumed to be the arithmetic sum of radiance contributed by haze and the radiance leaving the water surface. Reduction of haze is then carried out by a simple subtraction procedure. Images acquired over the Southern Tip of Palawan, Philippines are used for the experiments. Results show that the method works well for compensating signals contaminated by optically thin haze. Overcorrection occurs when haze is optically thick and geometrically complex. When images are acquired under hazy conditions the method can be applied to drastically improve image interpretability and may also be considered as a necessary pre-processing step for subsequent analyses and information extraction. © 2007 Elsevier Inc. All rights reserved. Keywords: Haze reduction; Shallow water marine environment; Landsat
1. Introduction The Landsat satellite sensor systems provide three visible channels that are capable of penetrating water columns with relatively high temporal and spatial resolution, and therefore have been the primary data source for studies of coral reef environment such as classification and discrimination of bottom types (e.g., Hochberg et al., 2003a; Lubin et al., 2001; Luczkovich et al., 1993), coral bleaching (e.g., Holden & LeDrew, 1998; Yamano & Tamura, 2004), reef environment change detection (e.g., Andréfouët et al., 2001; Zainal et al., 1993), among others. In addition, a large quantity of historical images is available thereby providing a potential for facilitating detection of dynamic changes in the aquatic environment. However, persistent cloud cover presents a prominent problem for image acquisition in the tropical ocean regions where the world reef systems are housed. As a result, available cloud-free images are scarce. Images acquired under suboptimal conditions may have to be used when alternative images are not
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available. In fact the contamination by spatially varying, semitransparent cloud and aerosol layers is a common problem that affects a significant portion of scenes within existing Landsat archives (Zhang et al., 2002). Reduction of haze is therefore indispensable when available images are acquired under such conditions, and must be viewed as a necessary preprocessing step to information extraction (Guindon & Zhang, 2002). In the visible spectrum, haze imposes an additive effect upon radiance reflected by the surface features. A number of scenebased algorithms are available for haze removal or reduction from the visible bands. The dark object subtraction is an image based algorithm for removing scene-wide homogeneous haze (see Chavez, 1988; Teillet & Fedosejevs, 1995). An extension of the dark object subtraction method to account for spatial variability of haze is to partition a scene into sub-regions and each is treated individually (see Teillet et al., 1987). Perhaps the following two algorithms are more effective in dealing with spatially varying haze on a pixel basis. One uses the TasseledCap Transforms (Kauth & Thomas, 1976) haze component (TC4) to estimate radiometric contribution from haze at each pixel location, and correction of haze is made by subtracting the
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Fig. 1. The linear relationship between the NIR band and the visible bands. Top row: the image segment taken from the March 5, 1989 scene (refer to Image Data and Processing Section) used for plotting the scatter-diagrams. The segment size is 209 pixels by 226 lines. Top row: (1) Band1. (2). Band3. (3). Band4. Bottom Row: Scatter plots of Band 4 against the visible bands. Grey tones represent density of points. Note in (4) the linear relationship no longer holds when saturation occurs in Band1.
Fig. 2. The scatter plots between the NIR and the visible bands using an image segment taken from the September 16, 1999 scene (Path/Row: 118/054). (1). Band1. The size of the image segment is 243 pixels by 133 lines. (2), (3), and (4) are the scatter plots of band4 against band1, band2 and band3 respectively. Grey tones represent density of points.
C.Y. Ji / Remote Sensing of Environment 112 (2008) 1773–1783 Table 1 List of images used for the current study Path/ row
Acquisition date
117/054 1989-03-05 117/054 1999-09-09
117/054 2001-04-07 118/054 1999-09-16
Sensor Cloud cover TM
Presence of severe haze over land and water ETM+ Relatively haze free, with 20% cloud cover mainly distributed over dry land areas ETM+ Light haze over shallow water and dry land ETM+ Severe cloud cover at the lower left portion over land and water. Wave patterns are apparent and water turbidity is high
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for the fixed transformation (refer to Crist et al., 1986; Crist & Cicone, 1984). For HOT transform, Zhang et al. (2002) state that the algorithm cannot be applied to correct areas that show higher brightness values such as sand, snow, and low DN values such as open water surface. In this study, a simple method for haze reduction from the visible bands over shallow water marine environment is presented. This method uses the NIR band to estimate the spatial distribution of haze intensity in the visible bands through a linear regression model. Reduction of haze is then carried out by a simple subtraction algorithm. 2. Methodology
amount of additive shift for each pixel (e.g., Lavreau 1991), or by histogram matching (e.g., Richter, 1996). The other is the socalled Haze Optimized Transform or HOT (Zhang et al., 2002). This approach uses haze-free areas to define a clear sky line in a two-dimensional spectral space (preferably the Red and the Blue spectra in the case of Landsat images). Hazy pixels tend to deviate from this line and the distance is measured as HOT response which represents the haziness of a pixel. By shifting the histogram of pixels exhibiting similar levels of HOT responses toward the histogram of a clear region, reduction of haze is achieved. In the case of shallow water marine environment however, neither the Tasseled Cap haze component approach nor the HOT transform method is suited for haze removal. This is because both algorithms tend to generate high haze intensity values for substrate types exhibiting high brightness values such as sandy bottoms and bleached coral. For the TC4, this is easily explained by the weight factors used
Haze refers to spatially varying, semitransparent cloud and aerosol layers (Zhang et al., 2002), which imposes an additive effect in the visible spectrum upon radiance from a ground pixel (Guindon & Zhang, 2002). However due to multiple scattering of incoming and outgoing radiations through the haze relative to the ground, the signal recorded from each ground pixel cannot be considered as the arithmetic sum of the radiance contributed by the haze and the radiance contributed by the ground pixel (Lavreau, 1991). To deal with such a complex issue, simplifications are required so that partial signal restoration can be made possible. In the current study, two assumptions are attempted to allow a first order approximation to the signal received at the sensor. The first assumption is that haze is optically thin enough to allow full transmittance of incoming and outgoing radiations. The second assumption is that haze only contributes to the upwelling radiance of the underlying surface and bottom
Fig. 3. The study area.
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Table 2 List of linear regression parameters for the two images Sensor/ Band 1989-03-05
2001-04-07
TM Band1 Band2 Band3 ETM+ Band1 Band2 Band3
R2
MSE⁎
Slope (α)
Intercept (β)
h0
2.2628 1.0492 1.3242
75.5311 14.6970 6.9703
83 21 12
0.9977 0.9996 0.9997
1.7924 0.3889 0.4035
23016 16218 16225
1.4932 1.5402 2.2216
47.2723 18.1992 16.7483
53 27 28
0.9953 0.9953 0.9944
0.3387 0.3378 0.5702
13040 14839 15639
Total Pixels
Table 3 Linear regression between the March 5, 1989 image (original and haze corrected) and the September 9, 1999 image Slope
Intercept
R2
Total pixels
0.5497 1.3541 0.6870
14.1675 2.1541 4.4114
0.6572 0.7969 0.6842
2141 2141 2141
0.6990 1.3360 0.6898
20.2736 18.6352 25.5343
0.9485 0.9082 0.8511
2141 2141 2141
Original
Outlier detection and deletion were conducted upon individual band (a data point detected as outlier in one band may not necessarily be an outlier in another band), resulting in different number of data points used for the least square fitting. ⁎ MSE is the mean standard error.
Band1 Band2 Band3 Corrected Band1 Band2 Band3
features, i.e., a single scattering process. Under these assumptions, the signal received at the sensor can be approximated by:
the radiance from water column) without the contamination from haze. H(x,y,i) is a measure related to haze intensity at pixel location (x,y) for band i. Image coordinates are represented by x (the column number), and y (the row number). If H(x,y,i) is known, removal of haze contamination can then be made by:
h0 DNTotal ð x;y;iÞ cDNð x;y;iÞ þ Hð x;y;iÞ
Total DNh0 ð x;y;iÞ cDNð x;y;iÞ Hð x;y;iÞ
i ¼ 1; 2; 3;
ð1Þ
where DNh0 i represents the signal directly related to the water leaving radiance (i.e. the sum of bottom-reflected radiance and
ð2Þ
A similar equation has been used by Lavreau (1991), except that the haze term is replaced by a quantity estimated from the TC4 for each spectral band. Hochberg et al. (2003b) present a method for removing the effect of sun glint caused by wave patterns from the visible bands of IKONOS data using the NIR band. The formulation of their algorithm is based on two optical properties of water. One is that water exhibits very strong absorption at the NIR wavelengths. This implies that, in the NIR spectrum, the reflectance of clear water is zero, and the reflectance of bottom features is also zero since the wavelengths in this spectrum do not penetrate water columns. The other is that the real refraction index of water is nearly equal at visible and NIR wavelengths, which signifies that the relative glint intensity is constant across all visible and NIR wavelengths (Hochberg et al., 2003b). For atmospheric haze over a shallow water marine environment, the spatial intensity in the visible bands can also be derived from the NIR band in a similar way. Following Bott (1991), Shettle and Fenn (1979), provides a plot of the real refraction index as a function of wavelength for three major types of aerosols (urban, rural, and oceanic) and for water. The real refraction index for all the types of aerosols is nearly constant across the visible and NIR spectrum. This implies that, although scattering increases
Table 4 Linear regression between the April 7, 2001 image (original and haze corrected) and the September 9, 1999 image R2
Total pixels
3.6968 2.4571 6.6066
0.7965 0.8772 0.7428
1972 1972 1972
12.930 13.6962 16.0684
0.8785 0.9133 0.8139
1972 1972 1972
Slope
Intercept
Band1 Band2 Band3
0.6464 1.1817 1.2287
Band1 Band2 Band3
0.6293 1.1558 1.2389
Original
Fig. 4. Results of haze correction applied to the 2001 image. (1). Original image. RGB-NBand321. (2). The same image after haze removal. The land area was superimposed onto the corrected image and no processing was carried out. Equal stretching was applied.
Corrected
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inversely with wavelength (the blue band experiences more scattering than does the NIR band), the relative scattering in the visible and NIR spectrum for maritime aerosols (as well as other types of aerosols) is independent of wavelength. In other words, haze imposes an equal degree of influence at visible and NIR wavelengths, and only the magnitude differs in each spectral band. Fig. 1 shows an image segment with a varying degree of haze and the scatter plots of the NIR band versus the visible bands using all pixels within the image segment. The strong linear relationship between any visible band and the NIR band over haze is observed over deep open water surface (with absence of bottom features in the visible bands), suggesting the
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assumption of wavelength independence of relative scattering in the visible-NIR spectrum may be true. In this regard, the algorithm developed by Hochberg et al. (2003b) for sun glint correction from the visible bands of IKONOS data could be directly extended to correct haze contaminated visible bands of Landsat TM and ETM+ images. The algorithm developed by Hochberg et al. (2003b) is to derive the absolute glint intensity by scaling the NIR image to a dynamic range of between 0 and 1 after masking out dry land areas (i.e., to obtain the relative spatial distribution of glint intensity) using the maximum and the minimum values found in glint affected areas in the NIR band. Values found at the same
Fig. 5. Scatter plots of mean values between 2001 image and 1999 image for each visible band (24 clusters). Left: before haze removal. Right: After haze removal. Total number of polygons: 24. Total number of pixels within the polygons: 4752.
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locations in a visible band are used to determine the scaling factor to transform the relative glint intensity to match the glint magnitude in that visible band (the absolute glint intensity). The estimated absolute glint intensity for a visible band is then subtracted from that visible band to obtain the de-glinted image. However, in the case of haze correction from Landsat TM and ETM+ images, this algorithm may not be easily extended due to various practical considerations. First, the maximum value of haze found in the NIR band may very well correspond to saturated values of digital numbers in a visible band (this is particularly true for the blue band of the 8-bit TM and ETM+ images as opposed to the 11-bit IKONOS data). Second, when
haze/thin cloud is geometrically complex (for instance, variations in altitude, presence of cast shadows, patchiness, etc.), and when other complicating factors such as the sun glint and the effects from suspended matters are present, the linear relationship between the visible and the NIR bands still holds but a great deal of dispersions is observed. It is quite clear from Fig. 2 that the dispersions of the scatter plots are the greatest at the lower ranges of the digital numbers (especially between the NIR and band1) where a minimum value has to be found in order to implement the algorithm. Third, the selection of the maximum and the minimum values can be influenced by uncorrelated and band-specific noise that may also influence the
Fig. 6. Scatter plots of mean values between 1989 image and 1999 image for each visible band (30 clusters). Left: before haze removal. Right: After haze removal. Total number of polygons: 30. Total number of pixels within the polygons: 4282.
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determination of the scaling factor. Nichol and Vohora (2004) state that a coherent pattern of system noise (noise other than the 16 line banding) observable on all visible wavebands of Landsat TM images over homogeneous surfaces such as waterbodies is regarded as serious enough to impair visual interpretation and affect image analysis results. Finally, Hochberg et al. (2003b) state that their model is truly valid only if the images are atmospherically corrected. When haze is present in a scene, the removal of haze should be carried out prior to the actual atmospheric correction (Richter, 1996). Under these circumstances, the outcome of haze removal using the two-point scaling method will depend on how well the linear relationship is depicted by the two points and it is likely that either undercorrection or overcorrection may occur in different parts of the image. As a result, a linear regression analysis that involves many data points is clearly a better choice to ensure that the best estimates of haze intensity in the visible bands are obtained. Linear regression analysis is therefore adopted in the current study and the details of the algorithm are given below. The first step in carrying out the linear regression analysis is to determine a proper threshold for the NIR band in order to separate haze contaminated areas from haze-free areas by a simple masking operation. This threshold can be obtained from statistics of a haze free area. For instance, the maximum brightness value found in a homogenous haze-free area can be used as a threshold (detection of outliers is required to ensure its statistical validity). The maximum brightness value from haze-free areas is used in order to exclude the modified radiance of clear water by materials such as dissolved materials, phytoplankton biomass, and possibly sediments from runoffs of river mouths (Maritorena, 1996). Since the task here is to remove haze, it is desirable to keep haze free areas in the visible bands intact, and only pixels exhibiting brightness values greater than the threshold will be altered. The same procedure can be used to select a proper threshold for each visible band, but the pixel location may differ in each band. Next, areas with varying intensities of haze are delineated by defining polygons on the image with visual interpretation. Pixels within these polygons are used to derive a linear regression model between the NIR and each of the visible bands. These regions contain only pixels from deep open water surface areas, no visible substrates should be present. Again, Lavreau (1991) used a similar procedure for estimating coefficients of a linear regression of visible bands against the Tasseled Cap haze component. For each visible band, a linear regression model is fit against the NIR band using only the pixels defined within the polygons, h0 Total h0 DNTotal ð x;y;iÞ DNð x;y;iÞ ¼ai DNð x;y;NIRÞ DNð x;y;NIRÞ þ bi
i ¼ 1; 2; 3;
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the NIR band. Once the parameters of the linear regression model are determined, the intensity of incremental haze H(x,y,i) for each visible band at each pixel location can then be estimated at: h0 h0 Hð x;y;iÞ ¼ ai DNTotal ð x;y;NIRÞ DNð x;y;NIRÞ þ bi DNð x;y;iÞ
ð4Þ
Correction of haze contaminated signals is then carried out according to (2). To retain radiometric precision, the whole
ð3Þ
h0 and DNh0 where DN(x,y,i) (x,y,NIR) are respectively the threshold values for a visible band and the NIR band (the brightness value over water with zero haze contamination), α and β are respectively the slope and the intercept of the linear regression model. The term Total DN(x,y,i) − DNh0 (x,y,i) refers to a visible band after masking out the Total zero haze areas, and DN(x,y,NIR) − DN h0 (x,y,NIR) is the same term for
Fig. 7. HOT and TC4 calculated for the March 5, 1989 image. (1). HOT. (2). TC4. (3). Band4 (NIR). All images were linearly stretched.
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procedure can be combined to directly generate the corrected image. 3. Image data and experimental tests Four Landsat images (Table 1) acquired over the Southern Tip of Palawan, Philippines and its vicinities are available for the current research. Fig. 3 depicts the geographical location of the area. Two images dated respectively March 5, 1989 and April 7, 2001 were used to demonstrate the proposed haze reduction algorithm. For each image, a number of polygons were defined on a true color composite over areas with absence of substrate types, and variations in terms of haze intensity were included. Pixels within these polygons form a sample set used for least square fitting between each of the visible band and the NIR band. During the least square fitting, all sample pixels with values of saturation were excluded. If an initial fitting yields an R2 value of over 0.99, the procedure terminates and the slope and the intercept are retained. Otherwise fitting continues by eliminating pixels with errors larger than a half of the standard deviation. Table 2 lists the linear regression coefficients for the two images. The absolute incremental haze intensity for each visible band was then calculated following Eq. (4), and the removal of haze was carried out according to Eq. (2). To evaluate the performance of the algorithm in a quantitative fashion, the images before and after haze correction were compared to a reference image. Areas that have not undergone changes were considered as Pseudo Invariant Targets or PITs (see Schott et al., 1988) for the purpose of comparison. Heo and Fitzhugh (2000) provide a summary on the criteria for selecting PITs. The image acquired on September 9, 1999 was used as the reference image. This image has roughly 20% cloud cover mostly over dry land and deep water areas. Over shallow water areas
there was no apparent haze present. Sea state was relatively calm as there were also no apparent windward wave patterns. Water clarity was also high. The two test images were geometrically registered to the reference image with RMS errors of less than one pixel. One test PIT dataset was extracted for each image. Assuming there has been no change occurred within the PITs, the closeness of the test dataset and the reference dataset can be measured by the agreement of a linear regression analysis. Outliers were not removed during the regression process, and the purpose of this was to calculate the absolute difference between the two datasets taken from the original and the corrected images. To further evaluate the results, test areas of bare sandy bottoms at varying depths with varying degrees of haze contamination were visually interpreted. The reasons for choosing sandy bottoms are two fold. One is that the aquatic environment in the study area is predominately covered by sandy bottoms. The other is that sandy bottoms at varying depths show a wide range of brightness variations. Therefore the assessment on sandy bottoms should reasonably reflect the general situation over the entire area. Test areas with a size ranging from several tens of pixels to more than a hundred pixels were identified. All test areas were chosen from relatively homogeneous regions to minimize the effect of image registration. Finally comparison of the proposed algorithm with the existing methods was also made and is presented in the Results section. 4. Results Fig. 4 presents the March 5, 1989 image before and after applying haze correction. Apparently the algorithm works well as it is evident that the corrected image looks very much haze free. In particular, boundaries of deep sandbars which were
Fig. 8. (1).True color composite of the image taken on September 16, 1999 (ETM+, Path/Row: 118/054). (2). Image after bulk correction.
C.Y. Ji / Remote Sensing of Environment 112 (2008) 1773–1783
Fig. 9. Comparison between the linear regression method and the two-point scaling algorithm extended from Hochberg et al. (2003b). Both (2) and (3) are linearly stretched in order to reveal the details.
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obscured by haze in the lower central portions are sharp on the corrected image. When brightness is saturated, the underlying signal cannot be restored. On the bottom-left portions of the image there has been overcorrection due to brightness saturation in the visible bands. Overcorrection was most severe in Band1 and minimal in Band3. Comparisons of the two PIT test datasets against the reference dataset are presented in Tables 3 and 4 respectively for the March 1989 image and the April 2001 image. Apparently for both dates there were marked improvements in the agreement between the haze-corrected images and the reference image over that between the original images and the reference image (the improvement of agreement based on R2 values of the regression ranges from 10% for Band3 of the 2001 image to 44.3% for Band1 of the 1989 image). Band1 of the 1989 image showed the largest agreement improvement because haze contamination was the most severe therefore the correction applied was the most effective in bringing down the errors introduced by haze. The remaining discrepancy between the test image and the reference image may be explained by numerous factors such as errors in image registration (see Townshend et al., 1992), differences in sun angle and atmospheric conditions, false PITs identified, and wind conditions that cause whitecaps contribution to the water-leaving radiance (Hu et al., 2001). Evidently the corrected images do show a much closer resemblance to the reference image. Figs. 5 and 6 present respectively comparisons of the two test images against the reference image on sandy bottoms for the 2001 image and the 1989 image. Again, improvements in terms of agreement with the reference image are seen over the entire dynamic range of brightness values. For the 2001 image, the test areas after haze removal are less scattered and tend to converge to the trend line showing more closeness to the 1999 image. The haze corrected 1989 image shows remarkable agreement with the 1999 image for all the three bands except the test areas at the high brightness range. These test areas were taken from a thick patch of haze over a submerged sand area and overcorrection has occurred over this area in all the visible bands. When haze is optically thick the assumptions used for the algorithm are no longer valid. Comparison between Fig. 5 and 6 seems to reveal that correction was better achieved for the 2001 image. This is because the image is less contaminated by haze, and the acquisition date is much closer to that of the reference image compared to the 1989 image. The 1989 image was acquired 10 years earlier than the reference image and a lot of changes could have occurred during this time period. In addition, this image was acquired by the older sensor (TM4) and the differences in sensor systems also contribute to the discrepancy between them. The proposed algorithm seems effective in compensating signals contaminated with optically thin haze. To compare with the existing methods, the HOT response image and the TC4 image for the 1989 scene are shown in Fig. 7. The HOT image was generated with coefficients estimated using data from a haze free area over land, whereas the TC4 was generated using all the 6 bands (excluding band6, which is the thermal band). The two images resemble each other in a number of ways. First, they both suppress the background well for the dry land areas.
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Second, they both show very high responses to the submerged sand. The characteristics of HOT and TC4 response over substrates make them unsuitable for the current task. On the other hand, haze is naturally separated from substrates on the NIR band. Particulate materials and dissolved matters appear to have a predominant role in the optical properties of island waters (Maritorena, 1996). When water clarity is low, the lower bound of haze intensity may not easily separate haze from turbid water pixels. Also, when sea surface is rough, the effect of sun glint can exceed the lower bound of haze intensity. Under these circumstances one may consider lowering the threshold used in Eq. (4) so that corrections can include the removal of sun glint and water turbidity while removing the haze. A bulk correction is then carried out. Fig. 8 presents the bulk correction applied to a scene acquired on September 16, 1999. Wave patterns and sun glint, the effect caused by water turbidity have apparently been removed along with the removal of haze. The scatter plots between the NIR and the visible bands (refer to Fig. 2) of this image show increased dispersions possibly due to the geometric complexity of the thin clouds, the presence of sun glint, and high water turbidity. A small segment is extracted from band 1 to demonstrate the differences between the two-point approach and the approach presented in the current study (Fig. 9). The two points used are the minimum and the maximum values chosen from the area shown. Evidently the correction is better achieved using the proposed method given in the present paper whereas the image corrected using the two-point approach extended from Hochberg et al. (2003b) shows more residual errors.
and absorption, an attempt has been made to simplify this process to allow a relative correction to the contaminated signals, so that information extraction from images acquired under hazy/cloudy conditions may still be possible to a certain extent. When cloud cover persists over a designated shallow water reef environment, image mosaics may be formed using cloud-free portions of many scenes (see Helmer & Ruefenacht, 2005), and the proposed algorithm could be useful to that effort.
5. Discussions
References
Since the proposed method uses the NIR band to derive haze intensity for the visible bands, sensor noise in the NIR band may have to be taken into account. If sensor noise is correlated between the visible bands and the NIR band, then removal of haze can also reduce sensor noise at the same time. For the 2001 scene, a hazefree and homogeneous area was checked for signal to noise ratio. The standard deviation was two times lower than that on the original image. Striping was much less visible on the corrected images. On the other hand, if the sensor noise is uncorrelated between the NIR and the visible bands, then correction of locally varying haze will boost the noise in the resultant image since the sensor noise in the NIR band will be compounded into the corrected image. This suggests that pre-processing of the NIR band can be applied to suppress sensor noise. One significant drawback of the proposed haze correction algorithm is overcorrection in certain areas with heavy haze contamination. Care must be taken when comparing the absolute magnitude of change in terms of radiometric properties of substrate types. For instance the difference between the radiances of healthy coral and bleached coral in the visible bands may be less than 3% (see Yamano & Tamura, 2004), and the error resulted from the removal of haze may exceed that level of precision leading to detection of false changes. Although the contamination of haze upon surface leaving radiance is a complex process that involves multiple scattering
Andréfouët, S., Muller-Karger, F. E., Hochberg, J. E., Hu, C., & Carder, K. L. (2001). Change detection in shallow coral reef environments using Landsat 7 ETM+data. Remote Sensing of Environment, 78, 150−162. Bott, A. (1991). On the influence of the physico-chemical properties of aerosols on the life cycle of radiation fogs. Boundary-Layer Meteorology, 56, 1−31. Chavez, P. S. (1988). An improved dark object subtraction technique for atmospheric scattering correction of multi-spectral data. Remote Sensing of Environment, 24, 450−479. Crist, E. P., & Cicone, R. C. (1984). A physically-based transformation of Thematic Mapper data — the TM Tasseled Cap. IEEE Transactions on Geoscience & Remote Sensing, 50, 343−352. Crist, E. P., Laurin, R., & Cicone, R. C. (1986). Vegetation and soils information contained in transformed Thematic Mapper data.. IGARSS'86, Zurich, vol. ESA SP-254 (pp. 14651472). ESA Publications Division. Guindon, B., & Zhang, Y. (2002). Robust haze reduction: an integral processing component in satellite-based land cover mapping. Symposium on Geospatial Theory, Processing and Applications, Ottawa. Helmer, E. H., & Ruefenacht, B. (2005). Cloud-free satellite image mosaics with regression trees and histogram matching. Photogrammetric Engineering & Remote Sensing, 71(9), 1079−1089. Heo, J., & Fitzhugh, T. W. (2000). A standardized radiometric normalization method for change detection using remotely sensed imagery. Photogrammetric Engineering & Remote Sensing, 66(2), 173−181. Hochberg, E. J., Atkinson, M. J., & Andréfouët, S. (2003). Spectral reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing. Remote Sensing of Environment, 85, 159−173. Hochberg, E. J., Andréfouët, S., & Tyler, M. R. (2003). Sea surface correction of high spatial resolution Ikonos images to improve bottom mapping in nearshore environments. IEEE Transactions on Geoscience and Remote Sensing, 41, 1724−1729.
6. Conclusions A haze reduction algorithm from the visible bands of Landsat TM and ETM+ images over a shallow water environment has been presented in this paper. The method can be applied to correct haze in the visible bands over an aquatic environment to a certain extent, depending on the conditions of haze present in an image. Since the method is based on the assumptions of single scattering in the radiometric interaction process, it can only be considered as a first order signal compensation. Overcorrection occurs when haze is optically thick. Further validation of the proposed algorithm is required. Care should be taken when subsequent quantitative analyses are to follow. Nevertheless, the proposed algorithm can be implemented to significantly improve the visual interpretability of images acquired under hazy conditions. Acknowledgement The author wishes to thank the anonymous referees for their most valued comments.
C.Y. Ji / Remote Sensing of Environment 112 (2008) 1773–1783 Holden, H., & LeDrew, E. (1998). Spectral discrimination of healthy and nonhealthy corals based on cluster analysis, principal components analysis and derivative spectroscopy. Remote Sensing of Environment, 65, 217−224. Hu, C., Muller-Karger, F. E., Andréfouët, S., & Carder, K. (2001). Atmospheric correction and cross-calibration of LANDSAT-7/ETM+imagery over aquatic environments: a multiplatform approach using Seawifs/MODIS. Remote Sensing of Environment, 78, 99−107. Kauth, R. J., & Thomas, G. S. (1976). The Tasseled Cap — a graphic description of the spectral-temporal development of the agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine processing of Remotely Sensed Data (pp. 41−51). Purdue University. Lavreau, J. (1991). De-hazing Landsat Thematic Mapper images. Photogrammetric Engineering & Remote Sensing, 57, 1297−1302. Lubin, D., Wei Li, Philip Dustan, & Maze, Charles H. (2001). Spectral signatures of coral reefs: features from space. Remote Sensing of Environment, 75, 127−137. Luczkovich, J. J., Wagner, T. W., Michalek, J. L., & Stoffle, R. W. (1993). Discrimination of coral reefs, seagrass meadows, and sandy bottom types from space: A Dominican Republic case study. Photogrammetric Engineering & Remote Sensing, 59(3), 385−389. Maritorena, S. (1996). Remote sensing of the water attenuation in coral reefs: a case study in French Polynesia. International Journal of Remote Sensing, 17(1), 155−166. Nichol, J. E., & Vohora, V. (2004). Noise over water surfaces in Landsat TM images. International Journal of Remote Sensing, 25(11), 2087−2094. Richter, R. (1996). Atmospheric correction of satellite data with haze removal including a haze/clear transition region. Computers & Geosciences, 22, 675−681.
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Schott, J. R., Salvaggio, C., & Volchok, W. J. (1988). Radiometric scene normalization using pseudoinvariant features. Remote Sensing of Environment, 26, 1−16. Shettle, E. P., & Fenn, R. W. (1979). Models of aerosols of lower troposphere and the effect of humidity variations on their optical properties. AFCRL Tech. Rep. 79 0214, Air Force Cambridge Research Laboratory, Hanscom Air Force Base, MA. 100 pp. Teillet, P. M., & Fedosejevs, G. (1995). On the dark target approach to atmospheric correction of remotely sensed data. Canadian Journal of Remote Sensing, 21, 374−386. Teillet, P. M., O'Neill, N. T., Kalinauskas, A., Stugeon, D., & Fedosejevs, G. (1987). A dynamic regression algorithm for incorporating atmospheric models into image correction procedures. Proceedings of the IGARSS'87 Symposium, Ann Arbor, MI (pp. 913−918). Townshend, J. R., Justice, C. O., Gurney, C., & McManus, J. (1992). The impact of misregistration on change detection. IEEE Transactions on Geoscience and Remote Sensing, 30, 1054−1060. Yamano, H., & Tamura, M. (2004). Detection limits of coral reef bleaching by satellite remote sensing: Simulation and data analysis. Remote Sensing of Environment, 90, 86−103. Zainal, A. J. M., Dalby, D. H., & Robinson, L. S. (1993). Monitoring marine ecological changes on the East Coast of Bahrain with Landsat TM. Photogrammetric Engineering & Remote Sensing, 59(3), 415−421. Zhang, Y., Guidon, B., & Cihlar, J. (2002). An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images. Remote Sensing of Environment, 82, 173−187.
Remote Sensing of Environment 112 (2008) 1773 – 1783 www.elsevier.com/locate/rse
Haze reduction from the visible bands of LANDSAT TM and ETM+ images over a shallow water reef environment C.Y. Ji Department of Geodetic Engineering, University of the Philippines Diliman, Quezon City 1101, Metro Manila, Philippines Received 18 July 2006; received in revised form 31 August 2007; accepted 1 September 2007
Abstract A method for haze reduction in the visible bands of Landsat TM and ETM+ images over a shallow water marine environment is presented in this paper. This method uses the near infrared (NIR) band to estimate the spatial distribution of haze intensity in each visible band through a linear regression model established over deep water areas. As a first order approximation, the signal received at the sensor is assumed to be the arithmetic sum of radiance contributed by haze and the radiance leaving the water surface. Reduction of haze is then carried out by a simple subtraction procedure. Images acquired over the Southern Tip of Palawan, Philippines are used for the experiments. Results show that the method works well for compensating signals contaminated by optically thin haze. Overcorrection occurs when haze is optically thick and geometrically complex. When images are acquired under hazy conditions the method can be applied to drastically improve image interpretability and may also be considered as a necessary pre-processing step for subsequent analyses and information extraction. © 2007 Elsevier Inc. All rights reserved. Keywords: Haze reduction; Shallow water marine environment; Landsat
1. Introduction The Landsat satellite sensor systems provide three visible channels that are capable of penetrating water columns with relatively high temporal and spatial resolution, and therefore have been the primary data source for studies of coral reef environment such as classification and discrimination of bottom types (e.g., Hochberg et al., 2003a; Lubin et al., 2001; Luczkovich et al., 1993), coral bleaching (e.g., Holden & LeDrew, 1998; Yamano & Tamura, 2004), reef environment change detection (e.g., Andréfouët et al., 2001; Zainal et al., 1993), among others. In addition, a large quantity of historical images is available thereby providing a potential for facilitating detection of dynamic changes in the aquatic environment. However, persistent cloud cover presents a prominent problem for image acquisition in the tropical ocean regions where the world reef systems are housed. As a result, available cloud-free images are scarce. Images acquired under suboptimal conditions may have to be used when alternative images are not
E-mail address: [email protected]. 0034-4257/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.rse.2007.09.006
available. In fact the contamination by spatially varying, semitransparent cloud and aerosol layers is a common problem that affects a significant portion of scenes within existing Landsat archives (Zhang et al., 2002). Reduction of haze is therefore indispensable when available images are acquired under such conditions, and must be viewed as a necessary preprocessing step to information extraction (Guindon & Zhang, 2002). In the visible spectrum, haze imposes an additive effect upon radiance reflected by the surface features. A number of scenebased algorithms are available for haze removal or reduction from the visible bands. The dark object subtraction is an image based algorithm for removing scene-wide homogeneous haze (see Chavez, 1988; Teillet & Fedosejevs, 1995). An extension of the dark object subtraction method to account for spatial variability of haze is to partition a scene into sub-regions and each is treated individually (see Teillet et al., 1987). Perhaps the following two algorithms are more effective in dealing with spatially varying haze on a pixel basis. One uses the TasseledCap Transforms (Kauth & Thomas, 1976) haze component (TC4) to estimate radiometric contribution from haze at each pixel location, and correction of haze is made by subtracting the
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Fig. 1. The linear relationship between the NIR band and the visible bands. Top row: the image segment taken from the March 5, 1989 scene (refer to Image Data and Processing Section) used for plotting the scatter-diagrams. The segment size is 209 pixels by 226 lines. Top row: (1) Band1. (2). Band3. (3). Band4. Bottom Row: Scatter plots of Band 4 against the visible bands. Grey tones represent density of points. Note in (4) the linear relationship no longer holds when saturation occurs in Band1.
Fig. 2. The scatter plots between the NIR and the visible bands using an image segment taken from the September 16, 1999 scene (Path/Row: 118/054). (1). Band1. The size of the image segment is 243 pixels by 133 lines. (2), (3), and (4) are the scatter plots of band4 against band1, band2 and band3 respectively. Grey tones represent density of points.
C.Y. Ji / Remote Sensing of Environment 112 (2008) 1773–1783 Table 1 List of images used for the current study Path/ row
Acquisition date
117/054 1989-03-05 117/054 1999-09-09
117/054 2001-04-07 118/054 1999-09-16
Sensor Cloud cover TM
Presence of severe haze over land and water ETM+ Relatively haze free, with 20% cloud cover mainly distributed over dry land areas ETM+ Light haze over shallow water and dry land ETM+ Severe cloud cover at the lower left portion over land and water. Wave patterns are apparent and water turbidity is high
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for the fixed transformation (refer to Crist et al., 1986; Crist & Cicone, 1984). For HOT transform, Zhang et al. (2002) state that the algorithm cannot be applied to correct areas that show higher brightness values such as sand, snow, and low DN values such as open water surface. In this study, a simple method for haze reduction from the visible bands over shallow water marine environment is presented. This method uses the NIR band to estimate the spatial distribution of haze intensity in the visible bands through a linear regression model. Reduction of haze is then carried out by a simple subtraction algorithm. 2. Methodology
amount of additive shift for each pixel (e.g., Lavreau 1991), or by histogram matching (e.g., Richter, 1996). The other is the socalled Haze Optimized Transform or HOT (Zhang et al., 2002). This approach uses haze-free areas to define a clear sky line in a two-dimensional spectral space (preferably the Red and the Blue spectra in the case of Landsat images). Hazy pixels tend to deviate from this line and the distance is measured as HOT response which represents the haziness of a pixel. By shifting the histogram of pixels exhibiting similar levels of HOT responses toward the histogram of a clear region, reduction of haze is achieved. In the case of shallow water marine environment however, neither the Tasseled Cap haze component approach nor the HOT transform method is suited for haze removal. This is because both algorithms tend to generate high haze intensity values for substrate types exhibiting high brightness values such as sandy bottoms and bleached coral. For the TC4, this is easily explained by the weight factors used
Haze refers to spatially varying, semitransparent cloud and aerosol layers (Zhang et al., 2002), which imposes an additive effect in the visible spectrum upon radiance from a ground pixel (Guindon & Zhang, 2002). However due to multiple scattering of incoming and outgoing radiations through the haze relative to the ground, the signal recorded from each ground pixel cannot be considered as the arithmetic sum of the radiance contributed by the haze and the radiance contributed by the ground pixel (Lavreau, 1991). To deal with such a complex issue, simplifications are required so that partial signal restoration can be made possible. In the current study, two assumptions are attempted to allow a first order approximation to the signal received at the sensor. The first assumption is that haze is optically thin enough to allow full transmittance of incoming and outgoing radiations. The second assumption is that haze only contributes to the upwelling radiance of the underlying surface and bottom
Fig. 3. The study area.
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Table 2 List of linear regression parameters for the two images Sensor/ Band 1989-03-05
2001-04-07
TM Band1 Band2 Band3 ETM+ Band1 Band2 Band3
R2
MSE⁎
Slope (α)
Intercept (β)
h0
2.2628 1.0492 1.3242
75.5311 14.6970 6.9703
83 21 12
0.9977 0.9996 0.9997
1.7924 0.3889 0.4035
23016 16218 16225
1.4932 1.5402 2.2216
47.2723 18.1992 16.7483
53 27 28
0.9953 0.9953 0.9944
0.3387 0.3378 0.5702
13040 14839 15639
Total Pixels
Table 3 Linear regression between the March 5, 1989 image (original and haze corrected) and the September 9, 1999 image Slope
Intercept
R2
Total pixels
0.5497 1.3541 0.6870
14.1675 2.1541 4.4114
0.6572 0.7969 0.6842
2141 2141 2141
0.6990 1.3360 0.6898
20.2736 18.6352 25.5343
0.9485 0.9082 0.8511
2141 2141 2141
Original
Outlier detection and deletion were conducted upon individual band (a data point detected as outlier in one band may not necessarily be an outlier in another band), resulting in different number of data points used for the least square fitting. ⁎ MSE is the mean standard error.
Band1 Band2 Band3 Corrected Band1 Band2 Band3
features, i.e., a single scattering process. Under these assumptions, the signal received at the sensor can be approximated by:
the radiance from water column) without the contamination from haze. H(x,y,i) is a measure related to haze intensity at pixel location (x,y) for band i. Image coordinates are represented by x (the column number), and y (the row number). If H(x,y,i) is known, removal of haze contamination can then be made by:
h0 DNTotal ð x;y;iÞ cDNð x;y;iÞ þ Hð x;y;iÞ
Total DNh0 ð x;y;iÞ cDNð x;y;iÞ Hð x;y;iÞ
i ¼ 1; 2; 3;
ð1Þ
where DNh0 i represents the signal directly related to the water leaving radiance (i.e. the sum of bottom-reflected radiance and
ð2Þ
A similar equation has been used by Lavreau (1991), except that the haze term is replaced by a quantity estimated from the TC4 for each spectral band. Hochberg et al. (2003b) present a method for removing the effect of sun glint caused by wave patterns from the visible bands of IKONOS data using the NIR band. The formulation of their algorithm is based on two optical properties of water. One is that water exhibits very strong absorption at the NIR wavelengths. This implies that, in the NIR spectrum, the reflectance of clear water is zero, and the reflectance of bottom features is also zero since the wavelengths in this spectrum do not penetrate water columns. The other is that the real refraction index of water is nearly equal at visible and NIR wavelengths, which signifies that the relative glint intensity is constant across all visible and NIR wavelengths (Hochberg et al., 2003b). For atmospheric haze over a shallow water marine environment, the spatial intensity in the visible bands can also be derived from the NIR band in a similar way. Following Bott (1991), Shettle and Fenn (1979), provides a plot of the real refraction index as a function of wavelength for three major types of aerosols (urban, rural, and oceanic) and for water. The real refraction index for all the types of aerosols is nearly constant across the visible and NIR spectrum. This implies that, although scattering increases
Table 4 Linear regression between the April 7, 2001 image (original and haze corrected) and the September 9, 1999 image R2
Total pixels
3.6968 2.4571 6.6066
0.7965 0.8772 0.7428
1972 1972 1972
12.930 13.6962 16.0684
0.8785 0.9133 0.8139
1972 1972 1972
Slope
Intercept
Band1 Band2 Band3
0.6464 1.1817 1.2287
Band1 Band2 Band3
0.6293 1.1558 1.2389
Original
Fig. 4. Results of haze correction applied to the 2001 image. (1). Original image. RGB-NBand321. (2). The same image after haze removal. The land area was superimposed onto the corrected image and no processing was carried out. Equal stretching was applied.
Corrected
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inversely with wavelength (the blue band experiences more scattering than does the NIR band), the relative scattering in the visible and NIR spectrum for maritime aerosols (as well as other types of aerosols) is independent of wavelength. In other words, haze imposes an equal degree of influence at visible and NIR wavelengths, and only the magnitude differs in each spectral band. Fig. 1 shows an image segment with a varying degree of haze and the scatter plots of the NIR band versus the visible bands using all pixels within the image segment. The strong linear relationship between any visible band and the NIR band over haze is observed over deep open water surface (with absence of bottom features in the visible bands), suggesting the
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assumption of wavelength independence of relative scattering in the visible-NIR spectrum may be true. In this regard, the algorithm developed by Hochberg et al. (2003b) for sun glint correction from the visible bands of IKONOS data could be directly extended to correct haze contaminated visible bands of Landsat TM and ETM+ images. The algorithm developed by Hochberg et al. (2003b) is to derive the absolute glint intensity by scaling the NIR image to a dynamic range of between 0 and 1 after masking out dry land areas (i.e., to obtain the relative spatial distribution of glint intensity) using the maximum and the minimum values found in glint affected areas in the NIR band. Values found at the same
Fig. 5. Scatter plots of mean values between 2001 image and 1999 image for each visible band (24 clusters). Left: before haze removal. Right: After haze removal. Total number of polygons: 24. Total number of pixels within the polygons: 4752.
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locations in a visible band are used to determine the scaling factor to transform the relative glint intensity to match the glint magnitude in that visible band (the absolute glint intensity). The estimated absolute glint intensity for a visible band is then subtracted from that visible band to obtain the de-glinted image. However, in the case of haze correction from Landsat TM and ETM+ images, this algorithm may not be easily extended due to various practical considerations. First, the maximum value of haze found in the NIR band may very well correspond to saturated values of digital numbers in a visible band (this is particularly true for the blue band of the 8-bit TM and ETM+ images as opposed to the 11-bit IKONOS data). Second, when
haze/thin cloud is geometrically complex (for instance, variations in altitude, presence of cast shadows, patchiness, etc.), and when other complicating factors such as the sun glint and the effects from suspended matters are present, the linear relationship between the visible and the NIR bands still holds but a great deal of dispersions is observed. It is quite clear from Fig. 2 that the dispersions of the scatter plots are the greatest at the lower ranges of the digital numbers (especially between the NIR and band1) where a minimum value has to be found in order to implement the algorithm. Third, the selection of the maximum and the minimum values can be influenced by uncorrelated and band-specific noise that may also influence the
Fig. 6. Scatter plots of mean values between 1989 image and 1999 image for each visible band (30 clusters). Left: before haze removal. Right: After haze removal. Total number of polygons: 30. Total number of pixels within the polygons: 4282.
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determination of the scaling factor. Nichol and Vohora (2004) state that a coherent pattern of system noise (noise other than the 16 line banding) observable on all visible wavebands of Landsat TM images over homogeneous surfaces such as waterbodies is regarded as serious enough to impair visual interpretation and affect image analysis results. Finally, Hochberg et al. (2003b) state that their model is truly valid only if the images are atmospherically corrected. When haze is present in a scene, the removal of haze should be carried out prior to the actual atmospheric correction (Richter, 1996). Under these circumstances, the outcome of haze removal using the two-point scaling method will depend on how well the linear relationship is depicted by the two points and it is likely that either undercorrection or overcorrection may occur in different parts of the image. As a result, a linear regression analysis that involves many data points is clearly a better choice to ensure that the best estimates of haze intensity in the visible bands are obtained. Linear regression analysis is therefore adopted in the current study and the details of the algorithm are given below. The first step in carrying out the linear regression analysis is to determine a proper threshold for the NIR band in order to separate haze contaminated areas from haze-free areas by a simple masking operation. This threshold can be obtained from statistics of a haze free area. For instance, the maximum brightness value found in a homogenous haze-free area can be used as a threshold (detection of outliers is required to ensure its statistical validity). The maximum brightness value from haze-free areas is used in order to exclude the modified radiance of clear water by materials such as dissolved materials, phytoplankton biomass, and possibly sediments from runoffs of river mouths (Maritorena, 1996). Since the task here is to remove haze, it is desirable to keep haze free areas in the visible bands intact, and only pixels exhibiting brightness values greater than the threshold will be altered. The same procedure can be used to select a proper threshold for each visible band, but the pixel location may differ in each band. Next, areas with varying intensities of haze are delineated by defining polygons on the image with visual interpretation. Pixels within these polygons are used to derive a linear regression model between the NIR and each of the visible bands. These regions contain only pixels from deep open water surface areas, no visible substrates should be present. Again, Lavreau (1991) used a similar procedure for estimating coefficients of a linear regression of visible bands against the Tasseled Cap haze component. For each visible band, a linear regression model is fit against the NIR band using only the pixels defined within the polygons, h0 Total h0 DNTotal ð x;y;iÞ DNð x;y;iÞ ¼ai DNð x;y;NIRÞ DNð x;y;NIRÞ þ bi
i ¼ 1; 2; 3;
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the NIR band. Once the parameters of the linear regression model are determined, the intensity of incremental haze H(x,y,i) for each visible band at each pixel location can then be estimated at: h0 h0 Hð x;y;iÞ ¼ ai DNTotal ð x;y;NIRÞ DNð x;y;NIRÞ þ bi DNð x;y;iÞ
ð4Þ
Correction of haze contaminated signals is then carried out according to (2). To retain radiometric precision, the whole
ð3Þ
h0 and DNh0 where DN(x,y,i) (x,y,NIR) are respectively the threshold values for a visible band and the NIR band (the brightness value over water with zero haze contamination), α and β are respectively the slope and the intercept of the linear regression model. The term Total DN(x,y,i) − DNh0 (x,y,i) refers to a visible band after masking out the Total zero haze areas, and DN(x,y,NIR) − DN h0 (x,y,NIR) is the same term for
Fig. 7. HOT and TC4 calculated for the March 5, 1989 image. (1). HOT. (2). TC4. (3). Band4 (NIR). All images were linearly stretched.
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procedure can be combined to directly generate the corrected image. 3. Image data and experimental tests Four Landsat images (Table 1) acquired over the Southern Tip of Palawan, Philippines and its vicinities are available for the current research. Fig. 3 depicts the geographical location of the area. Two images dated respectively March 5, 1989 and April 7, 2001 were used to demonstrate the proposed haze reduction algorithm. For each image, a number of polygons were defined on a true color composite over areas with absence of substrate types, and variations in terms of haze intensity were included. Pixels within these polygons form a sample set used for least square fitting between each of the visible band and the NIR band. During the least square fitting, all sample pixels with values of saturation were excluded. If an initial fitting yields an R2 value of over 0.99, the procedure terminates and the slope and the intercept are retained. Otherwise fitting continues by eliminating pixels with errors larger than a half of the standard deviation. Table 2 lists the linear regression coefficients for the two images. The absolute incremental haze intensity for each visible band was then calculated following Eq. (4), and the removal of haze was carried out according to Eq. (2). To evaluate the performance of the algorithm in a quantitative fashion, the images before and after haze correction were compared to a reference image. Areas that have not undergone changes were considered as Pseudo Invariant Targets or PITs (see Schott et al., 1988) for the purpose of comparison. Heo and Fitzhugh (2000) provide a summary on the criteria for selecting PITs. The image acquired on September 9, 1999 was used as the reference image. This image has roughly 20% cloud cover mostly over dry land and deep water areas. Over shallow water areas
there was no apparent haze present. Sea state was relatively calm as there were also no apparent windward wave patterns. Water clarity was also high. The two test images were geometrically registered to the reference image with RMS errors of less than one pixel. One test PIT dataset was extracted for each image. Assuming there has been no change occurred within the PITs, the closeness of the test dataset and the reference dataset can be measured by the agreement of a linear regression analysis. Outliers were not removed during the regression process, and the purpose of this was to calculate the absolute difference between the two datasets taken from the original and the corrected images. To further evaluate the results, test areas of bare sandy bottoms at varying depths with varying degrees of haze contamination were visually interpreted. The reasons for choosing sandy bottoms are two fold. One is that the aquatic environment in the study area is predominately covered by sandy bottoms. The other is that sandy bottoms at varying depths show a wide range of brightness variations. Therefore the assessment on sandy bottoms should reasonably reflect the general situation over the entire area. Test areas with a size ranging from several tens of pixels to more than a hundred pixels were identified. All test areas were chosen from relatively homogeneous regions to minimize the effect of image registration. Finally comparison of the proposed algorithm with the existing methods was also made and is presented in the Results section. 4. Results Fig. 4 presents the March 5, 1989 image before and after applying haze correction. Apparently the algorithm works well as it is evident that the corrected image looks very much haze free. In particular, boundaries of deep sandbars which were
Fig. 8. (1).True color composite of the image taken on September 16, 1999 (ETM+, Path/Row: 118/054). (2). Image after bulk correction.
C.Y. Ji / Remote Sensing of Environment 112 (2008) 1773–1783
Fig. 9. Comparison between the linear regression method and the two-point scaling algorithm extended from Hochberg et al. (2003b). Both (2) and (3) are linearly stretched in order to reveal the details.
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obscured by haze in the lower central portions are sharp on the corrected image. When brightness is saturated, the underlying signal cannot be restored. On the bottom-left portions of the image there has been overcorrection due to brightness saturation in the visible bands. Overcorrection was most severe in Band1 and minimal in Band3. Comparisons of the two PIT test datasets against the reference dataset are presented in Tables 3 and 4 respectively for the March 1989 image and the April 2001 image. Apparently for both dates there were marked improvements in the agreement between the haze-corrected images and the reference image over that between the original images and the reference image (the improvement of agreement based on R2 values of the regression ranges from 10% for Band3 of the 2001 image to 44.3% for Band1 of the 1989 image). Band1 of the 1989 image showed the largest agreement improvement because haze contamination was the most severe therefore the correction applied was the most effective in bringing down the errors introduced by haze. The remaining discrepancy between the test image and the reference image may be explained by numerous factors such as errors in image registration (see Townshend et al., 1992), differences in sun angle and atmospheric conditions, false PITs identified, and wind conditions that cause whitecaps contribution to the water-leaving radiance (Hu et al., 2001). Evidently the corrected images do show a much closer resemblance to the reference image. Figs. 5 and 6 present respectively comparisons of the two test images against the reference image on sandy bottoms for the 2001 image and the 1989 image. Again, improvements in terms of agreement with the reference image are seen over the entire dynamic range of brightness values. For the 2001 image, the test areas after haze removal are less scattered and tend to converge to the trend line showing more closeness to the 1999 image. The haze corrected 1989 image shows remarkable agreement with the 1999 image for all the three bands except the test areas at the high brightness range. These test areas were taken from a thick patch of haze over a submerged sand area and overcorrection has occurred over this area in all the visible bands. When haze is optically thick the assumptions used for the algorithm are no longer valid. Comparison between Fig. 5 and 6 seems to reveal that correction was better achieved for the 2001 image. This is because the image is less contaminated by haze, and the acquisition date is much closer to that of the reference image compared to the 1989 image. The 1989 image was acquired 10 years earlier than the reference image and a lot of changes could have occurred during this time period. In addition, this image was acquired by the older sensor (TM4) and the differences in sensor systems also contribute to the discrepancy between them. The proposed algorithm seems effective in compensating signals contaminated with optically thin haze. To compare with the existing methods, the HOT response image and the TC4 image for the 1989 scene are shown in Fig. 7. The HOT image was generated with coefficients estimated using data from a haze free area over land, whereas the TC4 was generated using all the 6 bands (excluding band6, which is the thermal band). The two images resemble each other in a number of ways. First, they both suppress the background well for the dry land areas.
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Second, they both show very high responses to the submerged sand. The characteristics of HOT and TC4 response over substrates make them unsuitable for the current task. On the other hand, haze is naturally separated from substrates on the NIR band. Particulate materials and dissolved matters appear to have a predominant role in the optical properties of island waters (Maritorena, 1996). When water clarity is low, the lower bound of haze intensity may not easily separate haze from turbid water pixels. Also, when sea surface is rough, the effect of sun glint can exceed the lower bound of haze intensity. Under these circumstances one may consider lowering the threshold used in Eq. (4) so that corrections can include the removal of sun glint and water turbidity while removing the haze. A bulk correction is then carried out. Fig. 8 presents the bulk correction applied to a scene acquired on September 16, 1999. Wave patterns and sun glint, the effect caused by water turbidity have apparently been removed along with the removal of haze. The scatter plots between the NIR and the visible bands (refer to Fig. 2) of this image show increased dispersions possibly due to the geometric complexity of the thin clouds, the presence of sun glint, and high water turbidity. A small segment is extracted from band 1 to demonstrate the differences between the two-point approach and the approach presented in the current study (Fig. 9). The two points used are the minimum and the maximum values chosen from the area shown. Evidently the correction is better achieved using the proposed method given in the present paper whereas the image corrected using the two-point approach extended from Hochberg et al. (2003b) shows more residual errors.
and absorption, an attempt has been made to simplify this process to allow a relative correction to the contaminated signals, so that information extraction from images acquired under hazy/cloudy conditions may still be possible to a certain extent. When cloud cover persists over a designated shallow water reef environment, image mosaics may be formed using cloud-free portions of many scenes (see Helmer & Ruefenacht, 2005), and the proposed algorithm could be useful to that effort.
5. Discussions
References
Since the proposed method uses the NIR band to derive haze intensity for the visible bands, sensor noise in the NIR band may have to be taken into account. If sensor noise is correlated between the visible bands and the NIR band, then removal of haze can also reduce sensor noise at the same time. For the 2001 scene, a hazefree and homogeneous area was checked for signal to noise ratio. The standard deviation was two times lower than that on the original image. Striping was much less visible on the corrected images. On the other hand, if the sensor noise is uncorrelated between the NIR and the visible bands, then correction of locally varying haze will boost the noise in the resultant image since the sensor noise in the NIR band will be compounded into the corrected image. This suggests that pre-processing of the NIR band can be applied to suppress sensor noise. One significant drawback of the proposed haze correction algorithm is overcorrection in certain areas with heavy haze contamination. Care must be taken when comparing the absolute magnitude of change in terms of radiometric properties of substrate types. For instance the difference between the radiances of healthy coral and bleached coral in the visible bands may be less than 3% (see Yamano & Tamura, 2004), and the error resulted from the removal of haze may exceed that level of precision leading to detection of false changes. Although the contamination of haze upon surface leaving radiance is a complex process that involves multiple scattering
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6. Conclusions A haze reduction algorithm from the visible bands of Landsat TM and ETM+ images over a shallow water environment has been presented in this paper. The method can be applied to correct haze in the visible bands over an aquatic environment to a certain extent, depending on the conditions of haze present in an image. Since the method is based on the assumptions of single scattering in the radiometric interaction process, it can only be considered as a first order signal compensation. Overcorrection occurs when haze is optically thick. Further validation of the proposed algorithm is required. Care should be taken when subsequent quantitative analyses are to follow. Nevertheless, the proposed algorithm can be implemented to significantly improve the visual interpretability of images acquired under hazy conditions. Acknowledgement The author wishes to thank the anonymous referees for their most valued comments.
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