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Spatial Filtering of Radar Data (RADARSAT) for Wetlands (Brackish Marshes) Classification
Spatial Filtering of Radar Data (RADARSAT) for Wetlands (Brackish Marshes) Classification Julie Noriega Rivera Rio* and Diego Fabia´n Lozano-Garcı´a*
O
ne of the main wetland areas for migratory bird breeding in Mexico is Laguna Madre, at State of Tamaulipas. It is imperative to have an adequate mapping of these ecosystems for management and protection. The objective of this work is to determine the most effective spatial filter treatment applied to a two-angle RADARSAT data set to reduce speckle effects and improve marsh classification performance of Tamaulipas wetlands. Eighteen spatial filter treatments were applied to the data based on four algorithms: Maximum a Posteriori, LeeSigma, Local Region, and Median. A supervised classification was applied to the filtered images with five resulting general classes. Overall classification performances were compared to determine statistical differences between filter treatments. An edge/boundary retention analysis and a visual assessment study were also applied to the data. It was determined that the Lee-Sigma algorithm with three iterations and square window sizes of three, five, and seven pixels, respectively, produced the best overall results with the 8-m pixel RADARSAT data. Elsevier Science Inc., 2000
INTRODUCTION Mexican coastal wetlands represent an important ecological and economic resource for the country. One of the main water systems for breeding migratory birds is the Laguna Madre, at the State of Tamaulipas, with an extension of 200,000 Ha. Its shallow and brackish waters provide suitable conditions for the establishment of
* ITESM, Centro de Calidad Ambiental, LabSIG, Monterrey, Nuevo Leo´n, Me´xico Address correspondence to J. Noriega Rivera Rio, ITESM, Centro de Calidad Ambiental, Edificio CEDES, Colonia Tecnolo´gico, 5 Piso, Ave. Eugenio Garza Sada #2501 Sur, 64849 Monterrey, Nuevo Leo´n, Me´xico. E-mail: [email protected] Accepted 18 January 2000. REMOTE SENS. ENVIRON. 73:143–151 (2000) Elsevier Science Inc., 2000 655 Avenue of the Americas, New York, NY 10010
aquatic and semiaquatic bird populations like ducks, geese, cormorants, and egrets, among others. To protect and manage the Laguna Madre, it is necessary to have an adequate inventory of its wetland conditions that includes its extension and seasonal variations. Ducks Unlimited de Mexico, A.C. (DUMAC) has developed mapping and inventory techniques for wetlands using Landsat Thematic Mapper. Results are encouraging; nevertheless, there are some limitations: (a) there is a lack of precision and detail using Landsat-TM for wetland inventories (compared with aerial photography or fieldwork); (b) in wetlands, it is difficult to distinguish between vegetation types; (c) the presence of clouds and clouds shadows affect the spectral values measured by Landsat-TM, creating confusion between vegetation types; (d) meteorological conditions can restrain image acquisitions; and (e) there are problems in defining the interface between water/soil boundaries in areas with dense vegetation. The area of a wetland is of ecological importance and is used for characterization. Landsat-TM limitations can be overcome with radar imagery, which is not affected by clouds. For instance, under certain conditions, radars have the ability to penetrate vegetation canopies, and they are sensitive to moisture changes in targets. Thus, under flooded conditions, the radar signal is enhanced due to a corner reflection effect between the water surface and vegetation stems or trunks. This enhancement occurs because the radar signal scattering at the water surface is specular, while it is diffuse at the soil surface. Radar images have a characteristic “salt and pepper” aspect known as “speckle noise.” Microwaves emitted by radar sensors travel in phase and interact minimally while traveling to their target, but after reaching it, these waves can interact to produce light and dark pixels. These pixel values do not reflect their real value measured on Earth, so it is necessary to eliminate them by filtering radar images before doing any processing to obtain satisfactory results. 0034-4257/00/$–see front matter PII S0034-4257(00)00089-4
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Figure 1. Study site: Laguna La Nacha, Tamaulipas, Mexico.
A mean filter is often used for reducing speckle noise in radar images, even though it is not effective in preserving boundaries between different pixel values because this algorithm averages the pixel values at the active window. Median filter has been widely used with satisfactory results by Goodenough et al. (1980), Henninger and Carney (1983), Mueller et al. (1987), and Mueller and Hoffer (1989). More sophisticated spatial filtering techniques have been developed by Heygster (1982), Hoffer et al. (1986, 1987), Touzi et al. (1988), Lopes et al. (1990; 1993), Nezry et al. (1991), and Mori et al. (1995). Algorithms from these latter works were used to process the RADARSAT images in this study.
treatments composed by different combinations of algorithms [Maximum a Posteriori (MAP), Lee-Sigma, Local Region, and Median], square window sizes (three, five, and seven), and number of iterations (one to four).
OBJECTIVE
METHODS
The main objective of this study was to identify an effective low-pass spatial filter treatment for reducing speckle noise effects in RADARSAT images, which would reflect accurately the seasonal environmental conditions while maintaining minimal loss in image resolution for brackish marsh wetlands of the Laguna Madre. Qualitative and quantitative evaluation methods were applied to filter
Study Site The study site is located at “Laguna La Nacha” and its surroundings, at San Fernando Municipality of Tamaulipas State, Mexico (Fig. 1). This lagoon is part of the wetland system of the Laguna Madre. Brackish marsh communities are represented by cattail (Typha domingensis), Scirpus californicus, Cyperus odoratus, and Arundo do-
Figure 2. RADARSAT Standard 1 (20⬚ incidence angle) and Standard 7 (45⬚ incidence angle) composite image from 2 and 6 March 1996, respectively.
Table 1. Characteristics of RADARSAT Images Scene Date Beam mode Polarization Wavelength Orbit Product type Size Image nominal size (km) # of Looks (range⫻azimuth) (m) Pixel spacing
March 2, 1996 Standard 1 (S1) (20–27⬚) HH C-band (5.6 cm) 1700-descending Path image plus 12,533 lines, 16,650 pixels 100⫻100 1⫻4 8m
March 6, 1996 Standard 7 (S7) (45–49⬚) HH C-band (5.6 cm) 1757-descending Path image plus 12,794 lines, 14,115 pixels 100⫻100 1⫻4 8m
RADARSAT Wetlands Classification
Table 2. Filter Treatments Applied to RADARSAT Data Algorithm Window Size (pixels)
Map
Lee-Sigma
Local Region
Median
3 3 5 7
3MAP1 3MAP2 5MAP3 7MAP4
3lesig1 3lesig2 5lesig3 7lesig4
3loc1 3loc2 5loc3 7loc4
3med1 3med2 5med3 7med4
nax. Floating vegetation consists of species of Eichornia crassipes, Pistia stratiotes, Nymphoides, and Ludwigia. Riparian vegetation is composed of species of Salix humboldtiana, Taxodium mucronatum, Astianthus viminalis, and Pachira aquatica. The flooded thorn bush has Mimosa pigra as a representative species, and the coastal dunes vegetation consists of Uniola paniculata and Croton punctulatus (Secretarı´a de Pesca, 1988; Contreras, 1993). “La Nacha” lagoon is very important for migratory birds because it is the only fresh water lagoon at the upper Laguna Madre of Tamaulipas complex. Its emergent vegetation is also significant as a breeding and sheltering site for birds, fishes, and crustaceans. Image Acquisition Two RADARSAT images were acquired (Table 1, Fig. 2). One was from Standard beam 1 (20⬚ incidence angle), and the second was from Standard beam 7 (45⬚ incidence angle). Remote sensing data were obtained almost simultaneously, minimizing the effects of water-level changes and land cover and phenology modifications. Image Filtering The advantages of the low-pass spatial filters used in this study are the following: (a) the median filter is effective in eliminating speckle noise while preserving boundaries between different cover types (Goodenough et al., 1980; Henninger and Carney, 1983; Mueller et al., 1985; Mueller and Hoffer, 1989); (b) the Lee-sigma algorithm is considered very effective in speckle reduction (Reinhardt, 1996; T. Albright, personal communication); (c) the Local Region filter provides an image with many uniform regions, and it is considered appropriate to use before an image classification; and (d) the Maximum a Posteriori (MAP) algorithm has been shown to be effective for wetland classification (Mori et al., 1995). Mean filter
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was not used because of its tendency to smooth boundaries, and boundaries were of extreme importance in this study. Eighteen different filter treatments were obtained (Table 2). The variations in parameters for each low-pass spatial filtering treatment were window size, number of iterations, the algorithm used, the coefficient of variation (for MAP and Lee-Sigma filters), and the number of standard deviations (for Lee-Sigma filter). Two combinations of filter treatments were also made: (a) Comb1, which consisted of three iterations of Lee-Sigma filter with square window sizes of three, five, and seven pixels, respectively (this combination is very effective in reducing speckle of radar satellite data); and (b) Comb2, a combination of Lee-Sigma and Local Region filters, which is appropriate prior to a classification (Table 3). Image Filtering Evaluation Evaluation of the filtering treatments was based on quantitative and qualitative assessment techniques, including cover-type classification, edge/boundary retention, and visual assessment. Cover-Type Classification A backscatter response file (training fields) was created by selection of areas (parametric samples) over RADARSAT Standard 1 (S1) and Standard 7 (S7) composite images. Feature space objects were selected over a feature space image (which consists of a S1 vs. S7 graph). Nonparametric backscatter responses (feature space objects) were then transformed into parametric ones by acquiring their digital values. Training fields were considered representative of the following classes: water, brackish marsh (represented by emergent macrophytes of genera Thypa and Scirpus), crops (irrigation crops), bush (high and dense bush, including riparian trees and road trees), and pasture (pasture, crops with few or no vegetation, bare soil, grass, flooded grass, etc.). Backscatter responses were evaluated through an iterative process of elimination, conjunction, and separation, using contingency matrices and divergence tests (Fig. 3a). Other methods used included (a) viewing the estimated area of a training field (using the parallelepiped decision rule) against a display of the original image (Alarm), and (b) evaluating class overlap, which was a comparison of ellipse diagrams of backscatter responses over a scatterplot of data values (bands S1 and S7). After the selection of backscatter responses, a super-
Table 3. Filter Treatment Combinations Applied to RADARSAT Data Combination
Treatment
Variation Coefficient
Sigma Value
Comb1 Comb2
(1) 3lesig1, (2) 5lesig2, (3) 7lesig3 (1) 3lesig1, (2) 5lesig2, (3) 7loc3
(1) 0.5, (2) 1.0, (3) 2.0 (1) 0.5, (2) 1.0, (3) 1.0
0.20 —
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Figure 3. (a) Selection of training fields and (b) classification flow diagram.
vised classification (Fig. 3b) was applied to the eighteen filter treatments and the unfiltered image. Backscatter classes were recoded into informational classes of water, brackish marsh, crop, bush, and pasture. To evaluate the supervised classifications, 76 test fields were selected based on (a) previous visits to the study site, (b) interpretation of aerial photographs, and (c) interpretation of an April 1990 Landsat-TM image. The percent of correctly classified pixels (PCC) were obtained for test fields of all filter treatments and the unfiltered image. An ANOVA was performed to determine differences between filter treatments. For the ANOVA, we must assume that observations are independent. Measurements of one object cannot interfere with other object measurements. Observations are measured from normal distributions, and groups must present equal variances (Schotzahauer and Littell, 1987). PCC were separated by classes and transformed with an arc sine function to convert the binomially distributed proportions (PCC) into a normal distribution. An analysis of variance (ANOVA) was obtained with a significance level of 5%, and the Studentized Newman-Keuls multiple range test (SNK) was applied to obtain statistical differences between filter treatments. This test was applied to one group with all PCC and to groups of PCC separated by
number of iterations for each algorithm and by algorithms for each iteration/window size. Edge/Boundary Retention The seven best filter treatments resulting from the ANOVA and SNK tests (for marsh class) were used in this test to recognize their edge/boundary retention properties. Transects crossing through different cover types were plotted against their pixels values. Each transect was three pixels wide (using its average value) and between 12 to 16 pixels long [based on the study of Mueller and Hoffer (1989)]. Transects from the seven filter treatments and from the unfiltered image were simultaneously plotted and visually compared to determine, with numbers from one (representing the nearest value to the unfiltered pixel value) to seven (the most different), the most proximal pixel value to the unfiltered pixel value (Fig. 4). Mean values were obtained from two or three different pixels for each transect, and those with ranges under three were determined to be the best boundary keeper. Visual Assessment The visual assessment was based on the following images: (a) individual S1 and S7 RADARSAT images filtered with each treatment and the unfiltered image; (b)
RADARSAT Wetlands Classification
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Figure 4. Edge/boundary transect. Represented classes are bush-waterbush.
composite (S1 and S7) RADARSAT images filtered with each treatment and the unfiltered image; (c) supervised classification of each filter treatment and the unfiltered image; (d) image transects from the best filter treatments, which resulted from the statistical analysis and which were used in the Edge/Boundary Retention analysis; (e) supervised classification of transects from the best filter treatments resulting from the statistical analysis and used in the edge/boundary retention analysis. Subsets were made at known places, and noise reduction vs. resolution loss was analyzed.
Table 4. Analysis of Variance (ANOVA) for Brackish Marsh Class Analysis of Variance procedure Class: filter
Levels: 19
Values NO MAP1 MAP2 MAP3 MAP4 Lesig1 Lesig2 Lesig3 Lesig4 Loc1 Loc2 Loc3 Loc4 Med1 Med2 Med3 Med4 Comb1 Comb2
Number of observations of data set⫽285 Dependent variable: ARC SINE
Source
DF
Square sum
F value
Pr⬎F
Model Error Corrected total
18 266 284
4.75806506 6.54761016 11.30567522
10.74
0.0001
R2, 0.420856; C.V., 13.77400; Arc Sine mean, 1.13904418 Filter
18
4.75806506
10.74
0.0001
RESULTS AND DISCUSSION Cover-Type Classification The ANOVA of the seventy-six field tests showed that the number of PCC and their variances for each class were different, therefore independent ANOVA was done for separated classes. The radar image presents different behaviors. Backscatter response depends on size, water content, roughness, and structure of subjects. For brackish marshes a corner reflector effect also occurs. In this paper we will discuss the results for a brackish marsh. For more information on behavior of other classes see Noriega (1997). The ANOVA for the brackish marsh is shown in Table 4. Fifteen field tests were used with a significance level of 5%. Parameters of the ANOVA show statistical differences between groups. The p value 0.0001 is minor than the 0.05 reference probability, which means that there are differences between filter treatments. Results of the supervised classification (Table 5) show that mean PCC of unfiltered image for brackish marsh is 78.99%, while PCC of 95.97%, 96.37%, and 94.92% correspond to 7MAP4, 7lesig4, and Comb1 filter treatments, respectively. The best improvement in classification is for Maximum a Posteriori and Lee-Sigma algorithms with four iterations and square window size of seven. As the number of iterations/window size increases in filter treatments, speckle is reduced and resolution is lost. Two iterations with a square window size of three does not suppress image speckle, for Lee-Sigma (Fig. 5) and Local Region filters. This is shown statistically in Ta-
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Table 5. SNK Results for Brackish Marsh; Comparison of the Number of Iterations for each Algorithm T (%)
7MAP4 (95.97)
5MAP3 (94.07)
3MAP2 (91.68)
3MAP1 (87.74)
NO (79.00)
T (%)
7lesig4 (96.36)
Comb1 (94.92)
Comb2 (90.55)
5lesig3 (90.07)
3lesig2 (83.01)
T (%)
7med4 (93.96)
5med3 (93.33)
3med2 (86.52)
3med1 (86.52)
NO (79.00)
T (%)
7loc4 (91.75)
Comb2 (90.55)
5loc3 (90.07)
3loc2 (83.83)
3loc1 (81.17)
3lesig1 (81.05)
NO (79.00)
NO (79.00)
T⫽treatment; % of precision.
ble 5, where the effects of the number of iterations for each algorithm are listed. There is a significant improvement between second and third iterations, but third and fourth iterations are statistically similar. Four iterations do not improve noise reduction, but decreases image resolution. Table 6 shows comparisons between algorithms for each iteration/window size. MAP and median filters present a significantly better average than the unfiltered image on the first iteration with a square window size of three pixels. Although Local Region and Lee-Sigma filters do not present significant improvements in speckle reduction until the third iteration with a square window size of five pixels, there is no improvement from the third to fourth iteration, and algorithms from three and four iterations are statistically similar. These results emphasize that three iterations are the most appropriate for noise reduction. Edge/Boundary Retention Median Algorithms show the best edge-preserving capacity, as shown in Table 7. These results agree with other reports (Heygster, 1982; Henninger and Carney, 1983; Mueller and Hoffer, 1989). MAP and Lee-Sigma (Comb1) filters with three iterations also show edge-preserving aptitudes. Pixel values from four iterations present different values as compared to the pixels from unfiltered images, showing greater loss of resolution. The best treatments from the SNK test (those with three iterations) and the edge/boundary retention analysis are 5MAP3, 5med5, and Comb1, which were preselected as the best filter treatments for the visual evaluation. Visual Assessment A visual analysis of radar and classification images showed speckle noise as brighter or darker points or spots inside a homogeneous area. Figure 6 shows a subset of the study area where brackish marshes, water, and pasture are present. Figures 5 and 7 show how enhanced or weakened pixel values (due to constructive or destructive interference of radar waves) result in variable
changes in class size and homogeneity. This results in mistakes related to mapping wetland extensions. A visual assessment of the three better classifications from the edge/boundary retention study shows that 5med3 still has speckle noise, such as pasture spots inside water, and vice versa (Fig. 8). Visual inspection of 5MAP3 (Fig. 9) and Comb1 (Fig. 10) images shows them to be very similar; nevertheless, a more detailed analysis shows greater resolution at Comb1 image. Circles on images show differences in speckle noise and resolution between these filter treatments. It is concluded that Combination 1 is an adequate filter treatment for RADARSAT 8 pixel-spacing data, showing acceptable resolution and noise reduction.
Figure 5. Subset from the study area of an extensive section of brackish marshes between La Nacha Lagoon and the Laguna Madre.
RADARSAT Wetlands Classification
Table 6. SNK Results for Brackish Marsh; Comparison of Algorithms for Each Iteration/Window Size T (%)
3MAP1 (87.74)
3med1 (86.52)
3loc1 (81.17)
3lesig1 (81.05)
NO (79.00)
T (%)
3MAP2 (91.68)
3med2 (88.52)
3loc2 (83.83)
3lesig2 (83.01)
NO (79.00)
T (%)
Comb1 (94.92)
5MAP3 (94.07)
5med3 (93.33)
Comb2 (90.55)
5lesig3 (90.07)
T (%)
7lesig4 (96.36)
7MAP4 (95.97)
7med4 (93.96)
7loc4 (91.75)
NO (79.00)
5loc3 (90.07)
NO (79.00)
Table 7. Better Filter Treatments in Descending Order for Each Transect Order
Transect 1
Transect 2
Transect 3
Transect 4
Transect 5
1⬚
5med3
5med3
5med3 7med4
5med3
5med3
2⬚
Comb1 7med4
7med4
5MAP3
7med4
Comb1 5MAP3
3⬚
5MAP3 7loc4
Comb1 5MAP3
7loc4
5MAP3 7loc4
7med4
4⬚
7lesig4
7loc4 7lesig4
Comb1
Comb1
7loc4
5⬚
7MAP4
7MAP4
7lesig4
7lesig 7MAP4
7lesig4
6⬚
Figure 6. Supervised classification of a subset of the RADARSAT S1 and S7 composite image without filtering.
7MAP4
7MAP4
Figure 7. Supervised classification of a subset of the RADARSAT S1 and S7 image filtered with the LeeSigma algorithm with two iterations and square window sizes of three.
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Noriega Rivera Rio and Fabia´n Lozano-Garcı´a
Figure 8. Supervised classification of a subset of the RADARSAT S1 and S7 image filtered with the median algorithm with three iterations and square window sizes of three, three, and five, respectively.
CONCLUSIONS Two iterations with a square window size of three were not enough to reduce speckle noise in RADARSAT data with 8-m pixel spacing. Lee-Sigma and Local Region algorithms are statistically similar with the unfiltered image. Three iterations for MAP, Lee-Sigma, Local Region, and Median filters present a significant improvement against the unfiltered image. Four iterations improves classification accuracy; however, this is not statistically better and there was a greater loss of resolution (shown in the results of the edge-preserving study and the visual assessment). Better treatments from the statistical test and the edge/boundary retention study were seen in 5med3, 5MAP3, and Comb1. A visual assessment showed that Comb1 (Lee-Sigma Algorithm with three iterations and a square window size of three, five, and seven, respectively) join together the best requirements, with high classification accuracy for brackish marsh (95%) and acceptable edge/boundary retention, with relatively low speckle noise and low loss in resolution. RADARSAT Int. and S. A. Niveles kindly donated one of the RADARSAT images since ITESM was their first client in Latin America. The April 1990 Landsat-TM image was donated by Ducks Unlimited of Mexico, A. C. (DUMAC).
Figure 9. Supervised classification of a subset of the RADARSAT S1 and S7 image filtered with the MAP algorithm with three iterations and square window sizes of three, three, and five, respectively.
Figure 10. Supervised classification of a subset of the RADARSAT S1 and S7 image filtered with the LeeSigma algorithm with three iterations and square window sizes of three, five, and seven, respectively. Circles on images show differences in speckle noise and resolution between filter treatments.
RADARSAT Wetlands Classification
REFERENCES Contreras, E. F. (1993), Ecosistemas Costeros Mexicanos, CONABIO, UAM, Unidad Ixtapalapa, Me´xico, D. F. Goodenough, D. G., Guindon, B., and Teillet, P. M. (1980), Correction of synthetic aperture radar and multispectral scanner data sets. In Proceedings of the Fifteenth International Symposium on Remote Sensing of Environment, Ann Arbor, USA, Vol. 2, pp. 259–270. Henninger, D. L., and Carney, J. H. (1983), Shuttle Imaging Radar-A (SIR-A) data as a complement to Landsat Multispectral Scanner (MSS) data. In International Geoscience and Remote Sensing Symposium (IGARSS’83), California, pp. 7.1–7.7. Heygster, G. (1982), Rank filters in digital image processing. Computer Graphics and Image Processing 19:148–164. Hoffer, R. M., Lozano-Garcı´a, D. F., and Gillespie, D. D. (1987), Characterizing forest stands with multi-incidence angle and multipolarized SAR data. Adv. Space Res., Great Britain 7:309–311. Hoffer, R. M., Lozano-Garcı´a, D. F., Gillespie, D. D., Mueller, P. W., and Ruzek, M. J. (1986), Analysis of multiple incidence angle SIR-B data for determining forest stand characteristics. In Second Spaceborne Imaging Radar Symposium, JPL, California, pp. 159–164. Lopes, A., Nezry, E., Touzi, R., and Laur, H. (1993), Structure detection and statistical adaptive speckle filtering in SAR images. Int. J. Remote Sens. 14:1735–1758. Lopes, A., Touzi, R., and Nezry, E. (1990), Adaptive speckle
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filters and scene heterogeneity. IEEE Trans. Geosci. Remote Sens. 28:992–1000. Mori, M., Yamamoto, N., and Yokoyama, T. (1995), Enhancement of JERS-1 SAR imagery. In Third Thematic Conference on Remote Sensing for Marine and Coastal Environments, Seattle, WA, Vol. II, pp. 801–807. Mueller, P. W. (1987), Spatial filtering of Shuttle Imaging Radar-B data. LARS-Purdue University, LARS Technical Report 091487, West Lafayette, Indiana, U.S.A. Mueller, P. W., and Hoffer, R. M. (1989), Low-pass spatial filtering of satellite radar data. Photogramm. Eng. Remote Sens. LV(6):887–897. Nezry, E., Lopes, A., and Touzi, R. (1991), Detection of structural and textural features for SAR images filtering. In Proceedings of IGARSS’91, pp. 2169–2172. Noriega, J. (1997), Evaluacio´n de Tratamientos de Filtrado Espacial aplicados a Ima´genes de Radar (RADARSAT) para Clasificacio´n de Humedales de tipo Tular, Tesis de Maestrı´a, ITESM, Campus Monterrey, Monterrey, N. L., Me´xico. Reinhardt, J. (1996), “Median”—a median filter, http://everest. radiology.uiowa.edu/tutor/image/improm/node5.html. Secretarı´a de Pesca (1988), Atlas del Golfo y Caribe de Me´xico, Diagno´stico Ambiental. Centro de Ecodesarrollo, Secretarı´a de Pesca, Me´xico, pp. 11–13. Schotzahauer, S., and Littell, L. (1987), SAS System for Elementary Statistical Analysis. SAS Institute Inc., North Carolina, p. 416. Touzi, R., Lopes, A., and Bousquest, P. (1988), A statistical and geometrical edge detector for SAR images. IEEE Trans. Geosci. Remote Sens. 26:764–773.
O
ne of the main wetland areas for migratory bird breeding in Mexico is Laguna Madre, at State of Tamaulipas. It is imperative to have an adequate mapping of these ecosystems for management and protection. The objective of this work is to determine the most effective spatial filter treatment applied to a two-angle RADARSAT data set to reduce speckle effects and improve marsh classification performance of Tamaulipas wetlands. Eighteen spatial filter treatments were applied to the data based on four algorithms: Maximum a Posteriori, LeeSigma, Local Region, and Median. A supervised classification was applied to the filtered images with five resulting general classes. Overall classification performances were compared to determine statistical differences between filter treatments. An edge/boundary retention analysis and a visual assessment study were also applied to the data. It was determined that the Lee-Sigma algorithm with three iterations and square window sizes of three, five, and seven pixels, respectively, produced the best overall results with the 8-m pixel RADARSAT data. Elsevier Science Inc., 2000
INTRODUCTION Mexican coastal wetlands represent an important ecological and economic resource for the country. One of the main water systems for breeding migratory birds is the Laguna Madre, at the State of Tamaulipas, with an extension of 200,000 Ha. Its shallow and brackish waters provide suitable conditions for the establishment of
* ITESM, Centro de Calidad Ambiental, LabSIG, Monterrey, Nuevo Leo´n, Me´xico Address correspondence to J. Noriega Rivera Rio, ITESM, Centro de Calidad Ambiental, Edificio CEDES, Colonia Tecnolo´gico, 5 Piso, Ave. Eugenio Garza Sada #2501 Sur, 64849 Monterrey, Nuevo Leo´n, Me´xico. E-mail: [email protected] Accepted 18 January 2000. REMOTE SENS. ENVIRON. 73:143–151 (2000) Elsevier Science Inc., 2000 655 Avenue of the Americas, New York, NY 10010
aquatic and semiaquatic bird populations like ducks, geese, cormorants, and egrets, among others. To protect and manage the Laguna Madre, it is necessary to have an adequate inventory of its wetland conditions that includes its extension and seasonal variations. Ducks Unlimited de Mexico, A.C. (DUMAC) has developed mapping and inventory techniques for wetlands using Landsat Thematic Mapper. Results are encouraging; nevertheless, there are some limitations: (a) there is a lack of precision and detail using Landsat-TM for wetland inventories (compared with aerial photography or fieldwork); (b) in wetlands, it is difficult to distinguish between vegetation types; (c) the presence of clouds and clouds shadows affect the spectral values measured by Landsat-TM, creating confusion between vegetation types; (d) meteorological conditions can restrain image acquisitions; and (e) there are problems in defining the interface between water/soil boundaries in areas with dense vegetation. The area of a wetland is of ecological importance and is used for characterization. Landsat-TM limitations can be overcome with radar imagery, which is not affected by clouds. For instance, under certain conditions, radars have the ability to penetrate vegetation canopies, and they are sensitive to moisture changes in targets. Thus, under flooded conditions, the radar signal is enhanced due to a corner reflection effect between the water surface and vegetation stems or trunks. This enhancement occurs because the radar signal scattering at the water surface is specular, while it is diffuse at the soil surface. Radar images have a characteristic “salt and pepper” aspect known as “speckle noise.” Microwaves emitted by radar sensors travel in phase and interact minimally while traveling to their target, but after reaching it, these waves can interact to produce light and dark pixels. These pixel values do not reflect their real value measured on Earth, so it is necessary to eliminate them by filtering radar images before doing any processing to obtain satisfactory results. 0034-4257/00/$–see front matter PII S0034-4257(00)00089-4
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Noriega Rivera Rio and Fabia´n Lozano-Garcı´a
Figure 1. Study site: Laguna La Nacha, Tamaulipas, Mexico.
A mean filter is often used for reducing speckle noise in radar images, even though it is not effective in preserving boundaries between different pixel values because this algorithm averages the pixel values at the active window. Median filter has been widely used with satisfactory results by Goodenough et al. (1980), Henninger and Carney (1983), Mueller et al. (1987), and Mueller and Hoffer (1989). More sophisticated spatial filtering techniques have been developed by Heygster (1982), Hoffer et al. (1986, 1987), Touzi et al. (1988), Lopes et al. (1990; 1993), Nezry et al. (1991), and Mori et al. (1995). Algorithms from these latter works were used to process the RADARSAT images in this study.
treatments composed by different combinations of algorithms [Maximum a Posteriori (MAP), Lee-Sigma, Local Region, and Median], square window sizes (three, five, and seven), and number of iterations (one to four).
OBJECTIVE
METHODS
The main objective of this study was to identify an effective low-pass spatial filter treatment for reducing speckle noise effects in RADARSAT images, which would reflect accurately the seasonal environmental conditions while maintaining minimal loss in image resolution for brackish marsh wetlands of the Laguna Madre. Qualitative and quantitative evaluation methods were applied to filter
Study Site The study site is located at “Laguna La Nacha” and its surroundings, at San Fernando Municipality of Tamaulipas State, Mexico (Fig. 1). This lagoon is part of the wetland system of the Laguna Madre. Brackish marsh communities are represented by cattail (Typha domingensis), Scirpus californicus, Cyperus odoratus, and Arundo do-
Figure 2. RADARSAT Standard 1 (20⬚ incidence angle) and Standard 7 (45⬚ incidence angle) composite image from 2 and 6 March 1996, respectively.
Table 1. Characteristics of RADARSAT Images Scene Date Beam mode Polarization Wavelength Orbit Product type Size Image nominal size (km) # of Looks (range⫻azimuth) (m) Pixel spacing
March 2, 1996 Standard 1 (S1) (20–27⬚) HH C-band (5.6 cm) 1700-descending Path image plus 12,533 lines, 16,650 pixels 100⫻100 1⫻4 8m
March 6, 1996 Standard 7 (S7) (45–49⬚) HH C-band (5.6 cm) 1757-descending Path image plus 12,794 lines, 14,115 pixels 100⫻100 1⫻4 8m
RADARSAT Wetlands Classification
Table 2. Filter Treatments Applied to RADARSAT Data Algorithm Window Size (pixels)
Map
Lee-Sigma
Local Region
Median
3 3 5 7
3MAP1 3MAP2 5MAP3 7MAP4
3lesig1 3lesig2 5lesig3 7lesig4
3loc1 3loc2 5loc3 7loc4
3med1 3med2 5med3 7med4
nax. Floating vegetation consists of species of Eichornia crassipes, Pistia stratiotes, Nymphoides, and Ludwigia. Riparian vegetation is composed of species of Salix humboldtiana, Taxodium mucronatum, Astianthus viminalis, and Pachira aquatica. The flooded thorn bush has Mimosa pigra as a representative species, and the coastal dunes vegetation consists of Uniola paniculata and Croton punctulatus (Secretarı´a de Pesca, 1988; Contreras, 1993). “La Nacha” lagoon is very important for migratory birds because it is the only fresh water lagoon at the upper Laguna Madre of Tamaulipas complex. Its emergent vegetation is also significant as a breeding and sheltering site for birds, fishes, and crustaceans. Image Acquisition Two RADARSAT images were acquired (Table 1, Fig. 2). One was from Standard beam 1 (20⬚ incidence angle), and the second was from Standard beam 7 (45⬚ incidence angle). Remote sensing data were obtained almost simultaneously, minimizing the effects of water-level changes and land cover and phenology modifications. Image Filtering The advantages of the low-pass spatial filters used in this study are the following: (a) the median filter is effective in eliminating speckle noise while preserving boundaries between different cover types (Goodenough et al., 1980; Henninger and Carney, 1983; Mueller et al., 1985; Mueller and Hoffer, 1989); (b) the Lee-sigma algorithm is considered very effective in speckle reduction (Reinhardt, 1996; T. Albright, personal communication); (c) the Local Region filter provides an image with many uniform regions, and it is considered appropriate to use before an image classification; and (d) the Maximum a Posteriori (MAP) algorithm has been shown to be effective for wetland classification (Mori et al., 1995). Mean filter
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was not used because of its tendency to smooth boundaries, and boundaries were of extreme importance in this study. Eighteen different filter treatments were obtained (Table 2). The variations in parameters for each low-pass spatial filtering treatment were window size, number of iterations, the algorithm used, the coefficient of variation (for MAP and Lee-Sigma filters), and the number of standard deviations (for Lee-Sigma filter). Two combinations of filter treatments were also made: (a) Comb1, which consisted of three iterations of Lee-Sigma filter with square window sizes of three, five, and seven pixels, respectively (this combination is very effective in reducing speckle of radar satellite data); and (b) Comb2, a combination of Lee-Sigma and Local Region filters, which is appropriate prior to a classification (Table 3). Image Filtering Evaluation Evaluation of the filtering treatments was based on quantitative and qualitative assessment techniques, including cover-type classification, edge/boundary retention, and visual assessment. Cover-Type Classification A backscatter response file (training fields) was created by selection of areas (parametric samples) over RADARSAT Standard 1 (S1) and Standard 7 (S7) composite images. Feature space objects were selected over a feature space image (which consists of a S1 vs. S7 graph). Nonparametric backscatter responses (feature space objects) were then transformed into parametric ones by acquiring their digital values. Training fields were considered representative of the following classes: water, brackish marsh (represented by emergent macrophytes of genera Thypa and Scirpus), crops (irrigation crops), bush (high and dense bush, including riparian trees and road trees), and pasture (pasture, crops with few or no vegetation, bare soil, grass, flooded grass, etc.). Backscatter responses were evaluated through an iterative process of elimination, conjunction, and separation, using contingency matrices and divergence tests (Fig. 3a). Other methods used included (a) viewing the estimated area of a training field (using the parallelepiped decision rule) against a display of the original image (Alarm), and (b) evaluating class overlap, which was a comparison of ellipse diagrams of backscatter responses over a scatterplot of data values (bands S1 and S7). After the selection of backscatter responses, a super-
Table 3. Filter Treatment Combinations Applied to RADARSAT Data Combination
Treatment
Variation Coefficient
Sigma Value
Comb1 Comb2
(1) 3lesig1, (2) 5lesig2, (3) 7lesig3 (1) 3lesig1, (2) 5lesig2, (3) 7loc3
(1) 0.5, (2) 1.0, (3) 2.0 (1) 0.5, (2) 1.0, (3) 1.0
0.20 —
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Noriega Rivera Rio and Fabia´n Lozano-Garcı´a
Figure 3. (a) Selection of training fields and (b) classification flow diagram.
vised classification (Fig. 3b) was applied to the eighteen filter treatments and the unfiltered image. Backscatter classes were recoded into informational classes of water, brackish marsh, crop, bush, and pasture. To evaluate the supervised classifications, 76 test fields were selected based on (a) previous visits to the study site, (b) interpretation of aerial photographs, and (c) interpretation of an April 1990 Landsat-TM image. The percent of correctly classified pixels (PCC) were obtained for test fields of all filter treatments and the unfiltered image. An ANOVA was performed to determine differences between filter treatments. For the ANOVA, we must assume that observations are independent. Measurements of one object cannot interfere with other object measurements. Observations are measured from normal distributions, and groups must present equal variances (Schotzahauer and Littell, 1987). PCC were separated by classes and transformed with an arc sine function to convert the binomially distributed proportions (PCC) into a normal distribution. An analysis of variance (ANOVA) was obtained with a significance level of 5%, and the Studentized Newman-Keuls multiple range test (SNK) was applied to obtain statistical differences between filter treatments. This test was applied to one group with all PCC and to groups of PCC separated by
number of iterations for each algorithm and by algorithms for each iteration/window size. Edge/Boundary Retention The seven best filter treatments resulting from the ANOVA and SNK tests (for marsh class) were used in this test to recognize their edge/boundary retention properties. Transects crossing through different cover types were plotted against their pixels values. Each transect was three pixels wide (using its average value) and between 12 to 16 pixels long [based on the study of Mueller and Hoffer (1989)]. Transects from the seven filter treatments and from the unfiltered image were simultaneously plotted and visually compared to determine, with numbers from one (representing the nearest value to the unfiltered pixel value) to seven (the most different), the most proximal pixel value to the unfiltered pixel value (Fig. 4). Mean values were obtained from two or three different pixels for each transect, and those with ranges under three were determined to be the best boundary keeper. Visual Assessment The visual assessment was based on the following images: (a) individual S1 and S7 RADARSAT images filtered with each treatment and the unfiltered image; (b)
RADARSAT Wetlands Classification
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Figure 4. Edge/boundary transect. Represented classes are bush-waterbush.
composite (S1 and S7) RADARSAT images filtered with each treatment and the unfiltered image; (c) supervised classification of each filter treatment and the unfiltered image; (d) image transects from the best filter treatments, which resulted from the statistical analysis and which were used in the Edge/Boundary Retention analysis; (e) supervised classification of transects from the best filter treatments resulting from the statistical analysis and used in the edge/boundary retention analysis. Subsets were made at known places, and noise reduction vs. resolution loss was analyzed.
Table 4. Analysis of Variance (ANOVA) for Brackish Marsh Class Analysis of Variance procedure Class: filter
Levels: 19
Values NO MAP1 MAP2 MAP3 MAP4 Lesig1 Lesig2 Lesig3 Lesig4 Loc1 Loc2 Loc3 Loc4 Med1 Med2 Med3 Med4 Comb1 Comb2
Number of observations of data set⫽285 Dependent variable: ARC SINE
Source
DF
Square sum
F value
Pr⬎F
Model Error Corrected total
18 266 284
4.75806506 6.54761016 11.30567522
10.74
0.0001
R2, 0.420856; C.V., 13.77400; Arc Sine mean, 1.13904418 Filter
18
4.75806506
10.74
0.0001
RESULTS AND DISCUSSION Cover-Type Classification The ANOVA of the seventy-six field tests showed that the number of PCC and their variances for each class were different, therefore independent ANOVA was done for separated classes. The radar image presents different behaviors. Backscatter response depends on size, water content, roughness, and structure of subjects. For brackish marshes a corner reflector effect also occurs. In this paper we will discuss the results for a brackish marsh. For more information on behavior of other classes see Noriega (1997). The ANOVA for the brackish marsh is shown in Table 4. Fifteen field tests were used with a significance level of 5%. Parameters of the ANOVA show statistical differences between groups. The p value 0.0001 is minor than the 0.05 reference probability, which means that there are differences between filter treatments. Results of the supervised classification (Table 5) show that mean PCC of unfiltered image for brackish marsh is 78.99%, while PCC of 95.97%, 96.37%, and 94.92% correspond to 7MAP4, 7lesig4, and Comb1 filter treatments, respectively. The best improvement in classification is for Maximum a Posteriori and Lee-Sigma algorithms with four iterations and square window size of seven. As the number of iterations/window size increases in filter treatments, speckle is reduced and resolution is lost. Two iterations with a square window size of three does not suppress image speckle, for Lee-Sigma (Fig. 5) and Local Region filters. This is shown statistically in Ta-
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Table 5. SNK Results for Brackish Marsh; Comparison of the Number of Iterations for each Algorithm T (%)
7MAP4 (95.97)
5MAP3 (94.07)
3MAP2 (91.68)
3MAP1 (87.74)
NO (79.00)
T (%)
7lesig4 (96.36)
Comb1 (94.92)
Comb2 (90.55)
5lesig3 (90.07)
3lesig2 (83.01)
T (%)
7med4 (93.96)
5med3 (93.33)
3med2 (86.52)
3med1 (86.52)
NO (79.00)
T (%)
7loc4 (91.75)
Comb2 (90.55)
5loc3 (90.07)
3loc2 (83.83)
3loc1 (81.17)
3lesig1 (81.05)
NO (79.00)
NO (79.00)
T⫽treatment; % of precision.
ble 5, where the effects of the number of iterations for each algorithm are listed. There is a significant improvement between second and third iterations, but third and fourth iterations are statistically similar. Four iterations do not improve noise reduction, but decreases image resolution. Table 6 shows comparisons between algorithms for each iteration/window size. MAP and median filters present a significantly better average than the unfiltered image on the first iteration with a square window size of three pixels. Although Local Region and Lee-Sigma filters do not present significant improvements in speckle reduction until the third iteration with a square window size of five pixels, there is no improvement from the third to fourth iteration, and algorithms from three and four iterations are statistically similar. These results emphasize that three iterations are the most appropriate for noise reduction. Edge/Boundary Retention Median Algorithms show the best edge-preserving capacity, as shown in Table 7. These results agree with other reports (Heygster, 1982; Henninger and Carney, 1983; Mueller and Hoffer, 1989). MAP and Lee-Sigma (Comb1) filters with three iterations also show edge-preserving aptitudes. Pixel values from four iterations present different values as compared to the pixels from unfiltered images, showing greater loss of resolution. The best treatments from the SNK test (those with three iterations) and the edge/boundary retention analysis are 5MAP3, 5med5, and Comb1, which were preselected as the best filter treatments for the visual evaluation. Visual Assessment A visual analysis of radar and classification images showed speckle noise as brighter or darker points or spots inside a homogeneous area. Figure 6 shows a subset of the study area where brackish marshes, water, and pasture are present. Figures 5 and 7 show how enhanced or weakened pixel values (due to constructive or destructive interference of radar waves) result in variable
changes in class size and homogeneity. This results in mistakes related to mapping wetland extensions. A visual assessment of the three better classifications from the edge/boundary retention study shows that 5med3 still has speckle noise, such as pasture spots inside water, and vice versa (Fig. 8). Visual inspection of 5MAP3 (Fig. 9) and Comb1 (Fig. 10) images shows them to be very similar; nevertheless, a more detailed analysis shows greater resolution at Comb1 image. Circles on images show differences in speckle noise and resolution between these filter treatments. It is concluded that Combination 1 is an adequate filter treatment for RADARSAT 8 pixel-spacing data, showing acceptable resolution and noise reduction.
Figure 5. Subset from the study area of an extensive section of brackish marshes between La Nacha Lagoon and the Laguna Madre.
RADARSAT Wetlands Classification
Table 6. SNK Results for Brackish Marsh; Comparison of Algorithms for Each Iteration/Window Size T (%)
3MAP1 (87.74)
3med1 (86.52)
3loc1 (81.17)
3lesig1 (81.05)
NO (79.00)
T (%)
3MAP2 (91.68)
3med2 (88.52)
3loc2 (83.83)
3lesig2 (83.01)
NO (79.00)
T (%)
Comb1 (94.92)
5MAP3 (94.07)
5med3 (93.33)
Comb2 (90.55)
5lesig3 (90.07)
T (%)
7lesig4 (96.36)
7MAP4 (95.97)
7med4 (93.96)
7loc4 (91.75)
NO (79.00)
5loc3 (90.07)
NO (79.00)
Table 7. Better Filter Treatments in Descending Order for Each Transect Order
Transect 1
Transect 2
Transect 3
Transect 4
Transect 5
1⬚
5med3
5med3
5med3 7med4
5med3
5med3
2⬚
Comb1 7med4
7med4
5MAP3
7med4
Comb1 5MAP3
3⬚
5MAP3 7loc4
Comb1 5MAP3
7loc4
5MAP3 7loc4
7med4
4⬚
7lesig4
7loc4 7lesig4
Comb1
Comb1
7loc4
5⬚
7MAP4
7MAP4
7lesig4
7lesig 7MAP4
7lesig4
6⬚
Figure 6. Supervised classification of a subset of the RADARSAT S1 and S7 composite image without filtering.
7MAP4
7MAP4
Figure 7. Supervised classification of a subset of the RADARSAT S1 and S7 image filtered with the LeeSigma algorithm with two iterations and square window sizes of three.
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Noriega Rivera Rio and Fabia´n Lozano-Garcı´a
Figure 8. Supervised classification of a subset of the RADARSAT S1 and S7 image filtered with the median algorithm with three iterations and square window sizes of three, three, and five, respectively.
CONCLUSIONS Two iterations with a square window size of three were not enough to reduce speckle noise in RADARSAT data with 8-m pixel spacing. Lee-Sigma and Local Region algorithms are statistically similar with the unfiltered image. Three iterations for MAP, Lee-Sigma, Local Region, and Median filters present a significant improvement against the unfiltered image. Four iterations improves classification accuracy; however, this is not statistically better and there was a greater loss of resolution (shown in the results of the edge-preserving study and the visual assessment). Better treatments from the statistical test and the edge/boundary retention study were seen in 5med3, 5MAP3, and Comb1. A visual assessment showed that Comb1 (Lee-Sigma Algorithm with three iterations and a square window size of three, five, and seven, respectively) join together the best requirements, with high classification accuracy for brackish marsh (95%) and acceptable edge/boundary retention, with relatively low speckle noise and low loss in resolution. RADARSAT Int. and S. A. Niveles kindly donated one of the RADARSAT images since ITESM was their first client in Latin America. The April 1990 Landsat-TM image was donated by Ducks Unlimited of Mexico, A. C. (DUMAC).
Figure 9. Supervised classification of a subset of the RADARSAT S1 and S7 image filtered with the MAP algorithm with three iterations and square window sizes of three, three, and five, respectively.
Figure 10. Supervised classification of a subset of the RADARSAT S1 and S7 image filtered with the LeeSigma algorithm with three iterations and square window sizes of three, five, and seven, respectively. Circles on images show differences in speckle noise and resolution between filter treatments.
RADARSAT Wetlands Classification
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