- Email: [email protected]
Automated geometric correction of multispectral images from High Resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B
ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Contents lists available at ScienceDirect
ISPRS Journal of Photogrammetry and Remote Sensing journal homepage: www.elsevier.com/locate/isprsjprs
Automated geometric correction of multispectral images from High Resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B Chabitha Devaraj a,⇑, Chintan A. Shah b a b
Electrical Engineering and Computer Science Department, South Dakota State University, SDEH 213, Box 2222, Brookings, SD 57007, United States Microsoft Corporation, Bing Imagery Technologies R&D, Boulder, CO 80302, United States
a r t i c l e
i n f o
Article history: Received 30 June 2013 Received in revised form 30 November 2013 Accepted 9 December 2013
Keywords: CBERS Orthorectification Geometric correction Geo-referencing Image registration Multi-temporal change
a b s t r a c t China–Brazil Earth Resource Satellite (CBERS) imagery is identified as one of the potential data sources for monitoring Earth surface dynamics in the event of a Landsat data gap. Currently available multispectral images from the High Resolution CCD (Charge Coupled Device) Camera (HRCC) on-board CBERS satellites (CBERS-2 and CBERS-2B) are not precisely geo-referenced and orthorectified. The geometric accuracy of the HRCC multispectral image product is found to be within 2–11 km. The use of CBERS-HRCC multispectral images to monitor Earth surface dynamics therefore necessitates accurate geometric correction of these images. This paper presents an automated method for geo-referencing and orthorectifying the multispectral images from the HRCC imager on-board CBERS satellites. Landsat Thematic Mapper (TM) Level 1T (L1T) imagery provided by the U.S. Geological Survey (USGS) is employed as reference for geometric correction. The proposed method introduces geometric distortions in the reference image prior to registering it with the CBERS-HRCC image. The performance of the geometric correction method was quantitatively evaluated using a total of 100 images acquired over the Andes Mountains and the Amazon rainforest, two areas in South America representing vastly different landscapes. The geometrically corrected HRCC images have an average geometric accuracy of 17.04 m (CBERS-2) and 16.34 m (CBERS2B). While the applicability of the method for attaining sub-pixel geometric accuracy is demonstrated here using selected images, it has potential for accurate geometric correction of the entire archive of CBERS-HRCC multispectral images. Ó 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
1. Introduction Multi-temporal remote sensing offers the capability to monitor Earth surface changes. Images acquired by Landsat satellite series provide Earth observation data starting from 1972. In the event of a Landsat data gap, the China–Brazil Earth Resource Satellite (CBERS) imagery is identified as one of the potential data sources for monitoring Earth surface dynamics (e.g., Powell et al., 2007; USGS, 2007). Since 2004, Brazil’s National Institute for Space Research (INPE) has made CBERS (CBERS-2 and CBERS-2B) imagery freely available to support scientific research. Currently available multispectral imagery from the High Resolution CCD (Charge Coupled Device) Camera (HRCC) on-board CBERS satellites are processed by Divisão de Geração de Imagens (DGI) to L2 level of correction (Sausen, 2001; Tominaga and Ferreira, 2008). The images processed to L2 level are only corrected for band-to-band misalignment and detector array displacement within each ⇑ Corresponding author. Tel.: +1 605 688 4664. E-mail addresses: [email protected] (C. Devaraj), Chintan.Shah@ Microsoft.com (C.A. Shah).
spectral band (INPE, 2004). The geometric accuracy of the HRCC multispectral image product is found to be approximately between 2 and 11 km (USGS, 2007). Therefore, erroneous association of CBERS-HRCC images with the Digital Elevation Model (DEM) data for orthorectification process may result in incorrect topographic correction. The illustration in Fig. 1a shows the CBERS-2 and CBERS-2B images acquired over the Amazon River, overlaid on a Landsat TM mosaic. Similarly, Fig. 1b shows the CBERS images acquired over the central Andes Mountain range overlaid on a Landsat TM mosaic. The Landsat TM images in Fig. 1 are processed by the U.S. Geological Survey (USGS) to L1T level of correction. A Landsat L1T image is precisely geo-referenced using ground control points and orthorectified by employing a Digital Elevation Model (DEM). The spatial discontinuity of the terrain features (e.g., rivers, lakes) at the boundary of Landsat TM and CBERS-HRCC images is evident from Fig. 1. This highlights the geometric inaccuracies in the CBERS images with respect to the Landsat TM images. The most widely used geometric correction methods use ground control points located on the image and the corresponding cartographic reference system in order to empirically determine a
0924-2716/$ - see front matter Ó 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.isprsjprs.2013.12.012
14
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Fig. 1. CBERS images acquired over (a) Amazon, and (b) Andes Mountains, overlaid on a Landsat TM mosaic. The spatially disjoint terrain features (e.g., rivers, lakes) near the CBERS-TM image boundary indicates geometric inaccuracies in the CBERS images.
mathematical transformation for correction of the image geometry (Novak, 1992; Toutin, 2004). Such methods require an expert operator for manual selection and matching of the control points whose cartographic co-ordinates are known from topographic field measurements (i.e., by GPS) or are derived from existing maps (Kardoulas et al., 1996; Smith and Atkinson, 2001). The increasing number of imaging sensors and the large amount of data collected every day necessitate automating this process. Hence, several researchers (e.g., Gao et al., 2009; Ghoshtasby, 1998; Gianinetto and Scaioni, 2008; Liu and Chen, 2009; Slonecker et al., 2009) have employed automated image registration methods for geometric correction. Such methods attempt to automatically extract a set of control points from the image pair to determine the geometric transformation that aligns the image to be corrected to the reference image. Both manual and automated geometric correction methods involve modeling the geometric distortions in the n-orthorectified image using a transformation function. The transformation ability of the model parameters, estimated using the control points, for a given transformation function depends on the type and severity of the geometric distortions present in the image (Sertel et al., 2007; Toutin, 2004). Therefore, determination of the appropriate transformation function is crucial to the accuracy of geometric correction. Since the form and amount of geometric distortion is unknown in most geometric correction applications, a plethora of local and global transformation models have been developed for this purpose (Novak, 1992; Toutin, 2004). Depending on the sensor type, the amount of local geometric distortions introduced by topographic variations, as well as the number and distribution of the control points, the user may need
to experiment with different transformation functions to identify the one that yields the best result (Novak, 1992; Tao and Hu, 2001; Toutin, 2004). To this end, we present an automated method that can perform accurate geometric correction by simply using a global affine transformation function. This is accomplished by introducing geometric distortions, caused by topographic variations and satellite viewing perspectives, in the reference image prior to registering it with the image to be geometrically corrected. In this paper, we refer to this process as simulating an un-orthorectified image or un-orthorectification. A similar approach of introducing geometric distortions in an orthorectified image was presented by Collins (1968) to create a stereo-pair. In addition to a DEM, the unorthorectification process requires the satellite altitude and the ground sampling distance (GSD) of the image to be geometrically corrected. Since the simulated un-orthorectified image and the image to be corrected are in the same image geometry after un-orthorectification, a simple affine transformation is sufficient for accurate image registration. The proposed method thus makes the transformation independent of the form and amount of geometric distortion. The image to be corrected is then spatially registered to the simulated un-orthorectified image and subsequently orthorectified using the DEM. We demonstrate the effectiveness of the proposed method by geometrically correcting a total of 100 HRCC multispectral images collectively acquired by CBERS-2 and CBERS-2B. The selected images are acquired over the Andes Mountains and the Amazon rainforest, two areas in South America representing vastly different landscapes. Quantitative evaluation of the method in geometrically
15
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
correcting CBERS-2 multispectral images results in an average Root Mean Square Error (RMSE) value of 0.85 pixel (17.04 m). Geometrically corrected CBERS-2B multispectral images have an average RMSE value of 0.82 pixel (16.34 m). The proposed method is fully automated and computationally efficient requiring less than 5 min for processing a CBERS-HRCC multispectral image on a desktop computing system with Windows 7 OS, Quad Core Intel 3.2 GHz processor, 12 GB memory, and a resident hard disk access. The paper is organized as follows. Section 2 describes the methodology and processing chain that allows for precise geo-referencing and orthorectification of CBERS-HRCC multispectral images. Section 3 presents the experimental results and Section 4 provides summary and conclusions. 2. Theory and methodology CBERS satellite imagery processed to L2 level of correction is distributed by Brazil’s INPE and can be accessed from http:// www.dgi.inpe.br/CDSR/. We selected images over two study sites in South America (Andes Mountains and Amazon rainforest) that represent vastly different landscapes. The study site in Andes Mountains is located at the border of Chile and Argentina extending between 23.12°S to 30.71°S and 62.21°W to 72.04°W. The landscape in Andes Mountains represents rugged mountainous topography and is dominated by many active volcanoes (e.g., Nevado Ojos del Salado and Llullaillaco) and associated landforms. The study site in Amazon is located in Brazil and extends between 0.52°N to 6.07°S and 50.23°W to 60.14°W. This study site is characterized as densely forested area that also includes the Amazon River and its tributaries. For each of the study sites we selected 25 images acquired by a particular CBERS satellite. Thus, we processed a total of 100 images collectively acquired by CBERS-2 and CBERS-2B. Acquisition dates for CBERS-2 and CBERS-2B images are provided in Table 1. The CBERS-HRCC images selected within the Andes study site have 10% or less cloud cover. Constant cloud cover in the Amazon rainforest, however, poses a challenge to obtain cloud-free
CBERS-HRCC images. Therefore, the CBERS-HRCC images selected within the Amazon study site contain 30% or less cloud cover. Corresponding Landsat TM image processed to L1T level acquired reasonably close to the date of the CBERS-HRCC image is selected based on the data quality and low cloud cover (610%) for both the study sites. The elevation information is obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) product distributed by METI and NASA. ASTER GDEM data are available on a 1 arc-second (approximately 30 m at the equator) grid and are referenced to the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. DEM data is processed to create seamless elevation data by removing residual bad/void values. Both Landsat TM and ASTER GDEM can be accessed from http:// earthexplorer.usgs.gov/. An overview of steps involved in the proposed geometric correction methodology is presented in Fig. 2. The Landsat TM data processed to L1T level of correction is used as the reference image for geometric correction (geo-referencing and orthorectification) of the multispectral images from CBERS-HRCC sensor. A Landsat L1T image is precisely geo-referenced using ground control points and orthorectified by employing a Digital Elevation Model (DEM). The un-orthorectification procedure introduces geometric distortions (caused by topographic variations and satellite viewing perspectives) in the TM image by utilizing a DEM and CBERS-HRCC sensor parameters (satellite altitude and GSD). The resulting simulated un-orthorectified image is identical to the CBERS-HRCC multispectral image in terms of geometric distortions. A geo-referenced CBERS-HRCC image is then generated by spatially registering the CBERS-HRCC image to the simulated un-orthorectified image. Finally, the geo-referenced CBERS-HRCC image is orthorectified using a DEM and CBERS-HRCC sensor parameters. A detailed description of individual steps involved in the proposed method is presented below. The geometry of topographic distortion in nadir viewing satellite imagery is shown in Fig. 3a. Due to the local topographic variations, the terrain surface at point A, which is at a height h (above
Table 1 CBERS-2 and CBERS-2B Path/Row and acquisition dates. #
Path/Row Andes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
173/128 173/129 173/130 173/131 173/132 174/128 174/129 174/130 174/131 174/132 175/128 175/129 175/130 175/131 175/132 176/128 176/129 176/130 176/131 176/132 177/128 177/129 177/130 177/131 177/132
Acquisition date
Acquisition date
CBERS-2
CBERS-2B
07/21/2005 07/21/2005 07/21/2005 07/21/2005 07/21/2005 05/01/2005 05/01/2005 05/01/2005 05/01/2005 05/01/2005 12/18/2005 12/18/2005 12/18/2005 12/18/2005 12/18/2005 03/04/2005 03/04/2005 03/04/2005 03/04/2005 03/04/2005 03/27/2005 03/27/2005 03/27/2005 03/27/2005 03/27/2005
07/05/08 07/05/08 07/05/08 07/05/08 07/05/08 07/02/08 07/02/08 07/02/08 07/02/08 07/02/08 07/25/08 07/25/08 07/25/08 07/25/08 07/25/08 05/05/08 05/05/08 05/05/08 05/05/08 05/05/08 05/02/08 05/02/08 05/02/08 05/02/08 05/02/08
Path/Row Amazon
166/101 166/102 166/103 166/104 166/105 167/101 167/102 167/103 167/104 167/105 168/101 168/102 168/103 168/104 168/105 169/101 169/102 169/103 169/104 169/105 170/101 170/102 170/103 170/104 170/105
Acquisition date
Acquisition date
CBERS-2
CBERS-2B
10/28/05 10/28/05 07/16/05 07/16/05 07/16/05 09/29/05 09/03/05 08/08/05 08/08/05 07/13/05 08/31/05 08/31/05 08/31/05 08/31/05 08/31/05 09/23/05 09/23/05 09/23/05 09/23/05 09/23/05 08/25/05 08/25/05 08/25/05 08/25/05 08/25/05
09/16/08 06/30/08 06/30/08 08/21/08 08/21/08 11/30/08 11/30/08 08/18/08 11/30/08 08/18/08 09/10/08 11/01/08 08/15/08 06/24/08 06/24/08 08/12/08 09/07/08 12/20/08 07/17/08 07/17/08 07/14/08 08/09/08 07/14/08 07/14/08 07/14/08
16
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Fig. 2. Overview of the geometric correction methodology.
(a)
(b)
Fig. 3. (a) Geometry of topographic distortion in nadir viewing satellite imagery, and (b) illustration of image un-orthorectification process.
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
17
Fig. 4. True color composite images (Path-176/Row-130) acquired over the Andes Mountains showing the world’s highest active volcano (Nevado Ojos del Salado 27.11°S, 68.54°W) and the salt lake of Chile (Laguna Verde 26.90°S, 68.47°W). The images shown here are the spatial subset of (a) CBERS-2. (b) Landsat TM, and (c) geometrically corrected CBERS-2 image scenes. The spatial misalignment between TM and CBERS-2 before and after geometric correction is estimated to be 3,274.26 m and 18.01 m, respectively.
18
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Fig. 5. True color composite images (Path-176/Row-128) acquired over the Atacama Desert near the Andes Mountains showing Llullaillaco volcano located at 24.72°S, 68.54°W and Laguna de la Azufrera lake located at 25.08°S, 68.51°W. The images shown here are the spatial subset of (a) CBERS-2B, (b) Landsat TM, and (c) geometrically corrected CBERS-2B image scenes. The spatial misalignment between TM and CBERS-2B before and after geometric correction is estimated to be 2025.44 m and 13.45 m, respectively.
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
19
Fig. 6. False color composite images (Path-168/Row-105) acquired over the state of Pará in Brazil showing the Tapajós River which is a major tributary of the Amazon River and the ‘‘fishbone’’ pattern of deforestation along the highways BR163 and BR230 (below the river). The images shown here are the spatial subset of (a) CBERS-2, (b) Landsat TM, and (c) geometrically corrected CBERS-2 image scenes. The spatial misalignment between TM and CBERS-2 before and after geometric correction is estimated to be 10,477.90 m and 14.55 m, respectively.
20
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Fig. 7. False color composite images (Path-170/Row-102) acquired over the Nhamundá River which creates the border between the Brazilian states of Pará (north) and Amazonas (south). The images shown here are the spatial subset of (a) CBERS-2B, (b) Landsat TM, and (c) geometrically corrected CBERS-2B image scenes. The spatial misalignment between TM and CBERS-2B before and after geometric correction is estimated to be 2,018.02 m and 18.09 m, respectively.
21
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Table 2 RMSE statistics for CBERS-2 and CBERS-2B images before and after geometric correction. CBERS-2 RMSE (m)
CBERS-2B RMSE (m)
Before
After
Before
After
Andes Mean Std. dev. Max Min
6609.96 3364.54 10542.73 2081.73
16.94 1.48 18.97 14.23
3229.44 911.38 4132.60 2025.44
15.73 1.39 17.75 13.45
Amazon Mean Std. dev. Max Min
9405.60 2057.82 10876.56 3220.00
17.14 1.50 19.21 14.26
2154.85 1414.00 5531.73 377.36
16.95 1.48 18.82 13.61
1 ðRe þ HÞ sinðb þ aÞ ðb þ aÞ c Re dS ¼ sin Re
ð1Þ
where
8 > <
9 h i > = ðRe þ HÞ sinðbÞ 1 ReRþh e qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi a ¼ tan1 > :ðRe þ hÞ 1 ½ðRe þHÞ sinðbÞ2 ðRe þ HÞ cosðbÞ> ; Re
ð2Þ
and Fig. 8. Geometric accuracy of CBERS-2 and CBERS-2B HRCC images acquired over the Andes Mountains (a) before geometric correction, and (b) after geometric correction.
2 1
b ¼ sin
3
Re sinðcÞ 6 7 4qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi5: 2 2 Re þ ðRe þ HÞ 2Re ðRe þ HÞ cosðcÞ
ð3Þ
The calculation of relief displacement in Eq. (1) requires the knowledge of (i) satellite altitude (H) above the reference datum, (ii) arithmetic mean radius of earth (Re ) for the reference datum, and (iii) the terrain elevation (h) above the reference datum at a given location estimated from a DEM. A detailed description of the sensor geometric model and mathematical derivations required for estimating relief displacement dS is provided in Gao et al. (2009) and Shah (2010). As the first step, in order to un-orthorectify an image, the image pixel corresponding to ground location A þ dS(A0 ) is assigned the value of an image pixel corresponding to ground location A. The un-orthorectification process can be explained using Fig. 3b, where ðx1 ; y1 Þ; ðx2 ; y2 Þ; ðx3 ; y3 Þandðx4 ; y4 Þ correspond to the image corners. The start ðx12 ; y12 Þ and end ðx34 ; y34 Þ co-ordinates of the orbital track are the mid-points of the upper and lower image corners, respectively. If the orbital track on image is defined by the generalized line equation Ax þ By þ C ¼ 0, then the orientation of the orbital track with respect to the vertical line (North) is calculated as
2 1
h ¼ sin
3
jx12 x34 j 6 7 4qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi5: 2 2 ðx12 x34 Þ þ ðy12 y34 Þ
ð4Þ
Thus, if ds is the pixel unit displacement of ðxo ; yo Þ along the scanline in the image, then the pixel co-ordinates of the displaced location in the image are estimated as
xu ¼ xo þ k ds cosðhÞ Fig. 9. Geometric accuracy of CBERS-2 and CBERS-2B HRCC images acquired over the Amazon rainforest (a) before geometric correction, and (b) after geometric correction.
where
0
the reference datum), is imaged at point A . This results in a relief displacement of dS units on ground (Fig. 3a), which is calculated as follows (Shah, 2010),
ð5Þ
yu ¼ yo þ k ds sinðhÞ
k¼
8 > < 1; > : þ1;
Axo þByo þC
pffiffiffiffiffiffiffiffiffiffi P 0 2 2 A þB
Axo þByo þC
pffiffiffiffiffiffiffiffiffiffi < 0 2 2 A þB
:
ð6Þ
22
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Fig. 10. Geometrically corrected CBERS images acquired over (a) Amazon, and (b) Andes Mountains, overlaid on a Landsat TM mosaic. The high geometric accuracies attained using our method is indicated by the perfect alignment at the CBERS-TM image boundaries resulting in spatial continuity of different terrain features.
In Eqs. (5) and (6), ðxo ; yo Þ correspond to the image co-ordinates of an orthorectified pixel, whereas ðxu ; yu Þ denote its un-orthorectified location in image co-ordinates. The pixel unit displacement ds in Eq. (5) is calculated from the corresponding ground relief displacement (dS) determined in Eq. (1) as
dS ds ¼ GSD
ð7Þ
The next step comprises of geo-referencing the CBERS-HRCC image by registering it with the un-orthorectified image simulated using the TM image. This is accomplished by identifying a set of control points from the image pair using an automated image matching technique. There exist several methods for automated selection of control points (e.g., Gianinetto and Scaioni, 2008; Kennedy and Cohen, 2003; Liu and Chen, 2009; Mikolajczyk and Schmid, 2005). One might make use of any commercial image analysis software or develop their own method for the automated selection of control points. We selected the control points using the automated area-based matching method available in ENVIÒ. For this method, we chose the Förstner interest point operator, a minimum normalized cross correlation of 0.7, a moving window size of 11 pixels, an area chip size of 256 pixels, and a search window size of 101 pixels. The extracted control points are then used to estimate the parameters of a 2D affine transformation function for registration. Since the proposed method makes the two images geometrically alike prior to registration, a simple global transformation is sufficient to accurately model the spatial misalignment. As the final step, geometric distortions caused by topographic variations and satellite viewing perspectives are removed by
orthorectifying the geo-referenced CBERS-HRCC image. For a given pixel at location ðxu ; yu Þ in the geo-referenced image, its corresponding orthorectified pixel location is calculated as
xo ¼ xu k ds cosðhÞ yo ¼ yu k ds sinðhÞ
ð8Þ
DEM and CBERS-HRCC sensor parameters are used in calculating the relief displacement dS (Eq. (1)) and the corresponding pixel unit displacement ds in the image is calculated using Eq. (7). The HRCC instrument on-board CBERS-2 and CBERS-2B acquires images with 113 km swath width at 20 m GSD while orbiting at an altitude of 778 km above the reference datum. The Landsat TM images distributed by the USGS have a GSD of 30 m and 180 km swath width. Before simulating the un-orthorectified image, the Landsat TM image is resampled to 20 m through a bilinear interpolation to match the CBERS-HRCC image GSD. Also, four or more TM images are mosaicked to ensure complete overlap with a CBERSHRCC scene. In order to create a ‘‘radiometrically’’ seamless TM mosaic, the TM image DN (digital number) values are converted to exo-atmospheric reflectance (Schott, 2007). This process of converting the DN values to spectral radiance and then normalizing for the solar irradiance reduces the inter-scene variability. However, in cases when the calibration coefficients for radiance conversion are not available, DN values can be directly used in the TM mosaic generation process. Finally, the TM mosaic is projected on the UTM zone determined by the CBERS-HRCC scene center. In many cases, the CBERS-HRCC images are misaligned with respect to the TM image by more than a kilometer. Hence, prior to un-orthorectification,
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
a coarse spatial alignment is performed between the TM mosaic and the CBERS-HRCC image using a Fast Fourier Transform (FFT)based image matching method (e.g., Kuglin and Hines, 1975). In most cases, a simple translation along x and/or y axis is sufficient to approximately align the image with the resampled TM mosaic. The main purpose of this coarse image registration is to ensure that the Landsat TM mosaic roughly covers the same scene on the ground as that covered by the CBERS-HRCC image. To geometrically correct the multispectral bands, the near infra-red spectral band (0.77–0.89 lm in CBERS-HRCC and 0.77– 0.90 lm in TM) is used for coarse image registration as well as for estimating the parameters of 2D affine transformation function in the geo-referencing step. However, one can use any spectral band that is common to both CBERS-HRCC and TM for this purpose. Since all the spectral bands on HRCC are accurately co-registered with each other, the parameters estimated using the near infra-red spectral band are subsequently used to geometrically correct all other (blue: 0.45–0.52 lm, green: 0.52–0.59 lm, and red: 0.63–0.69 lm) CBERS-HRCC multispectral bands.
3. Experimental results The performance of the proposed method in geometrically correcting the CBERS-HRCC multispectral images is evaluated using images acquired over two selected study sites in South America (Andes Mountains and Amazon rainforest) that represent vastly different landscapes. Figs. 4–7 exhibit the performance of the proposed method in geometrically correcting the multispectral bands acquired by the HRCC sensor on-board CBERS-2 and CBERS-2B satellites. In these illustrations, true color composite images are shown for the Andes study site (Figs. 4 and 5) and false color (color infra-red) composite images are shown for the Amazon study site (Figs. 6 and 7). A significant amount of spatial misalignment between Landsat TM and the CBERS images prior to geometric correction is evident from these figures. Accurate geo-referencing and orthorectification of the multispectral bands from the CBERS-HRCC sensor using the proposed method result in images with sub-pixel accuracies. The plots in Figs. 8 (Andes) and 9 (Amazon) provide the calculated spatial alignment errors (RMSE) between Landsat TM and the CBERS-HRCC images before and after geometric correction. The RMSE for each of the 100 images is calculated using a set of 30 ground control points that are fairly evenly distributed throughout the image. These control points were manually selected by identifying prominent surface features that were common between the image pair. The large values of RMSE before correction can be attributed to the large geo-referencing inaccuracies in the CBERS images. The results presented in Figs. 8 and 9 indicate that the RMSE values of the corrected CBERS images have significantly decreased, resulting in sub-pixel accuracies. Table 2 presents the spatial alignment error (RMSE) statistics for the CBERS images before and after geometric correction. The mean and standard deviation values of RMSE are much lower for the geometrically corrected CBERS images. Also, the maximum RMSE values (<20 m) after geometric correction confirm the robustness of the method in geometrically correcting the images over dynamic landscapes. Fig. 10(a) and (b) corresponds to the geometrically corrected CBERS images overlaid on the Landsat TM mosaic. Spatial continuity along the image boundary (when compared with Fig. 1) demonstrates perfect spatial alignment between the corrected CBERS-HRCC multispectral and the L1T Landsat TM images. This further confirms the high geometric accuracies in CBERS-2 and CBERS-2B HRCC multispectral imagery attained using the proposed method.
23
4. Summary and conclusions CBERS satellite imagery is identified as one of the potential data sources for monitoring Earth surface dynamics in the event of a Landsat data gap. Currently available multispectral imagery from the HRCC instrument on-board CBERS satellites (CBERS-2 and CBERS-2B) are processed to L2 level of correction by the DGI. CBERS images processed to L2 level are not precisely geo-referenced and orthorectified. CBERS-HRCC image product accuracy is expected to be in the order of 2–11 km. Therefore, the use of CBERS-HRCC multispectral images to monitor Earth surface changes necessitates accurate geometric correction of these images. In this paper we presented a novel method for automatic geo-referencing and orthorectification of CBERS-HRCC multispectral images. The method is used for geometric correction of CBERS-HRCC images using a Landsat TM image as the reference image. The novelty of our method is that it introduces geometric distortions, caused by topographic variations and satellite viewing perspectives, in the reference image prior to registering it with the CBERS-HRCC image. Since the simulated un-orthorectified image and the image to be corrected are in the same image geometry after un-orthorectification, a simple affine transformation is sufficient for accurate image registration. Thus, unlike the existing geometric correction methods, our method makes the image transformation independent of the form and amount of geometric distortion. Therefore, the significance of our method is that the user does not need to experiment with different transformation functions to identify the one that yields the best geometric correction result. The robustness of the proposed method in geometrically correcting the images over dynamic landscapes is demonstrated using CBERS-2 and CBERS-2B images acquired over the Amazon rainforests and Andes mountain range. Geometrically corrected CBERS-2 and CBERS-2B HRCC images have an average RMSE value of 0.85 pixel and 0.82 pixel, respectively. These low average RMSE values of the CBERS-HRCC images indicate the high geometric accuracy attained using the method presented in this paper. While the applicability of this method is demonstrated here using selected CBERS images, it has potential for accurate geometric correction of the entire archive of CBERS-HRCC multispectral images. Acknowledgment The authors would like to thank the anonymous reviewers for their constructive comments on this manuscript. References Collins, S.H., 1968. Stereoscopic orthophoto maps. The canadian, surveyor 22, 167– 176. Gao, F., Masek, J.G., Wolfe, R.E., 2009. Automated registration and orthorectification package for landsat and landsat-like data processing. J. Appl. Rem. Sens. 3, 033515–033535. Ghoshtasby, A., 1998. Image registration by local approximation. Image Vision Comput. 6, 255–261. Gianinetto, M., Scaioni, M., 2008. Automated geometric correction of highresolution pushbroom satellite data. Photogram. Eng. Rem. Sens. 74, 107–116. INPE (2004). Geometric quality assessment of CBERS-2. (). Kardoulas, N.G., Bird, A.C., Lawan, A.I., 1996. Geometric correction of SPOT and Landsat imagery: a comparison of map- and GPS-derived control points. Photogram. Eng. Rem. Sens. 62, 1173–1177. Kennedy, R.E., Cohen, W.B., 2003. Automated designation of tie-points for image-toimage co-registration. Int. J. Rem. Sens. 24, 3467–3490. Kuglin, C.D., Hines, D.C., 1975. The phase correlation image alignment method. Proc. Int. Conf. Cyber. Soc. 4, 163–165. Liu, C., Chen, P., 2009. Automatic extraction of ground control regions and orthorectification of remote sensing imagery. Opt. Exp. 17, 7970–7984. Mikolajczyk, K., Schmid, C., 2005. A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Machine Int. 27, 1615–1630. Novak, K., 1992. Rectification of digital imagery. Photogram. Eng. Rem. Sens. 58, 339–344.
24
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Powell, S.L., Pflugmacher, D., Kirschbaum, A.A., Kim, Y., Cohen, W.B., 2007. Moderate resolution remote sensing alternatives: a review of landsat-like sensors and their applications. J. Appl. Rem. Sens. 1, 1–16. Sausen, T.M., 2001. The China–Brazil Earth Resources Satellite (CBERS). ISPRS Soc. 6, 27–28. Schott, J.R., 2007. Remote Sensing: The Image Chain Approach, second ed. Oxford University Press. Sertel, E., Kutoglu, S.H., Kaya, S., 2007. Geometric correction accuracy of different satellite sensor images: application of figure condition. Int. J. Rem. Sens. 28, 4685–4692. Shah, C.A. (2010). Assessment of high-latitude arctic lake dynamics in representative areas on continuous permafrost. Ph.D. Dissertation, Dept. Geography, University of California, Los Angeles (UCLA).
Slonecker, E.T., Johnson, B., McMahon, J., 2009. Automated imagery orthorectification pilot. J. Appl. Rem. Sens. 3, 033552–033568. Smith, D.P., Atkinson, S.F., 2001. Accuracy of rectification using topographic map versus GPS ground control points. Photogram. Eng. Rem. Sens. 67, 565–570. Tao, C.V., Hu, Y., 2001. A comprehensive study of the rational function model for photogrammetric processing. Photogram. Eng. Rem. Sens. 67, 1347–1357. Tominaga, J., Ferreira, M., 2008. Payload operations of CBERS satellite series by INPE. Space Oper. Commun. 5, 19–23. Toutin, T., 2004. Geometric processing of remote sensing images: models, algorithms and methods. Int. J. Rem. Sens. 25, 1893–1924. USGS 2007. Landsat Data Gap Study. Technical Report: DCN Version 1.0..
Contents lists available at ScienceDirect
ISPRS Journal of Photogrammetry and Remote Sensing journal homepage: www.elsevier.com/locate/isprsjprs
Automated geometric correction of multispectral images from High Resolution CCD Camera (HRCC) on-board CBERS-2 and CBERS-2B Chabitha Devaraj a,⇑, Chintan A. Shah b a b
Electrical Engineering and Computer Science Department, South Dakota State University, SDEH 213, Box 2222, Brookings, SD 57007, United States Microsoft Corporation, Bing Imagery Technologies R&D, Boulder, CO 80302, United States
a r t i c l e
i n f o
Article history: Received 30 June 2013 Received in revised form 30 November 2013 Accepted 9 December 2013
Keywords: CBERS Orthorectification Geometric correction Geo-referencing Image registration Multi-temporal change
a b s t r a c t China–Brazil Earth Resource Satellite (CBERS) imagery is identified as one of the potential data sources for monitoring Earth surface dynamics in the event of a Landsat data gap. Currently available multispectral images from the High Resolution CCD (Charge Coupled Device) Camera (HRCC) on-board CBERS satellites (CBERS-2 and CBERS-2B) are not precisely geo-referenced and orthorectified. The geometric accuracy of the HRCC multispectral image product is found to be within 2–11 km. The use of CBERS-HRCC multispectral images to monitor Earth surface dynamics therefore necessitates accurate geometric correction of these images. This paper presents an automated method for geo-referencing and orthorectifying the multispectral images from the HRCC imager on-board CBERS satellites. Landsat Thematic Mapper (TM) Level 1T (L1T) imagery provided by the U.S. Geological Survey (USGS) is employed as reference for geometric correction. The proposed method introduces geometric distortions in the reference image prior to registering it with the CBERS-HRCC image. The performance of the geometric correction method was quantitatively evaluated using a total of 100 images acquired over the Andes Mountains and the Amazon rainforest, two areas in South America representing vastly different landscapes. The geometrically corrected HRCC images have an average geometric accuracy of 17.04 m (CBERS-2) and 16.34 m (CBERS2B). While the applicability of the method for attaining sub-pixel geometric accuracy is demonstrated here using selected images, it has potential for accurate geometric correction of the entire archive of CBERS-HRCC multispectral images. Ó 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
1. Introduction Multi-temporal remote sensing offers the capability to monitor Earth surface changes. Images acquired by Landsat satellite series provide Earth observation data starting from 1972. In the event of a Landsat data gap, the China–Brazil Earth Resource Satellite (CBERS) imagery is identified as one of the potential data sources for monitoring Earth surface dynamics (e.g., Powell et al., 2007; USGS, 2007). Since 2004, Brazil’s National Institute for Space Research (INPE) has made CBERS (CBERS-2 and CBERS-2B) imagery freely available to support scientific research. Currently available multispectral imagery from the High Resolution CCD (Charge Coupled Device) Camera (HRCC) on-board CBERS satellites are processed by Divisão de Geração de Imagens (DGI) to L2 level of correction (Sausen, 2001; Tominaga and Ferreira, 2008). The images processed to L2 level are only corrected for band-to-band misalignment and detector array displacement within each ⇑ Corresponding author. Tel.: +1 605 688 4664. E-mail addresses: [email protected] (C. Devaraj), Chintan.Shah@ Microsoft.com (C.A. Shah).
spectral band (INPE, 2004). The geometric accuracy of the HRCC multispectral image product is found to be approximately between 2 and 11 km (USGS, 2007). Therefore, erroneous association of CBERS-HRCC images with the Digital Elevation Model (DEM) data for orthorectification process may result in incorrect topographic correction. The illustration in Fig. 1a shows the CBERS-2 and CBERS-2B images acquired over the Amazon River, overlaid on a Landsat TM mosaic. Similarly, Fig. 1b shows the CBERS images acquired over the central Andes Mountain range overlaid on a Landsat TM mosaic. The Landsat TM images in Fig. 1 are processed by the U.S. Geological Survey (USGS) to L1T level of correction. A Landsat L1T image is precisely geo-referenced using ground control points and orthorectified by employing a Digital Elevation Model (DEM). The spatial discontinuity of the terrain features (e.g., rivers, lakes) at the boundary of Landsat TM and CBERS-HRCC images is evident from Fig. 1. This highlights the geometric inaccuracies in the CBERS images with respect to the Landsat TM images. The most widely used geometric correction methods use ground control points located on the image and the corresponding cartographic reference system in order to empirically determine a
0924-2716/$ - see front matter Ó 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.isprsjprs.2013.12.012
14
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Fig. 1. CBERS images acquired over (a) Amazon, and (b) Andes Mountains, overlaid on a Landsat TM mosaic. The spatially disjoint terrain features (e.g., rivers, lakes) near the CBERS-TM image boundary indicates geometric inaccuracies in the CBERS images.
mathematical transformation for correction of the image geometry (Novak, 1992; Toutin, 2004). Such methods require an expert operator for manual selection and matching of the control points whose cartographic co-ordinates are known from topographic field measurements (i.e., by GPS) or are derived from existing maps (Kardoulas et al., 1996; Smith and Atkinson, 2001). The increasing number of imaging sensors and the large amount of data collected every day necessitate automating this process. Hence, several researchers (e.g., Gao et al., 2009; Ghoshtasby, 1998; Gianinetto and Scaioni, 2008; Liu and Chen, 2009; Slonecker et al., 2009) have employed automated image registration methods for geometric correction. Such methods attempt to automatically extract a set of control points from the image pair to determine the geometric transformation that aligns the image to be corrected to the reference image. Both manual and automated geometric correction methods involve modeling the geometric distortions in the n-orthorectified image using a transformation function. The transformation ability of the model parameters, estimated using the control points, for a given transformation function depends on the type and severity of the geometric distortions present in the image (Sertel et al., 2007; Toutin, 2004). Therefore, determination of the appropriate transformation function is crucial to the accuracy of geometric correction. Since the form and amount of geometric distortion is unknown in most geometric correction applications, a plethora of local and global transformation models have been developed for this purpose (Novak, 1992; Toutin, 2004). Depending on the sensor type, the amount of local geometric distortions introduced by topographic variations, as well as the number and distribution of the control points, the user may need
to experiment with different transformation functions to identify the one that yields the best result (Novak, 1992; Tao and Hu, 2001; Toutin, 2004). To this end, we present an automated method that can perform accurate geometric correction by simply using a global affine transformation function. This is accomplished by introducing geometric distortions, caused by topographic variations and satellite viewing perspectives, in the reference image prior to registering it with the image to be geometrically corrected. In this paper, we refer to this process as simulating an un-orthorectified image or un-orthorectification. A similar approach of introducing geometric distortions in an orthorectified image was presented by Collins (1968) to create a stereo-pair. In addition to a DEM, the unorthorectification process requires the satellite altitude and the ground sampling distance (GSD) of the image to be geometrically corrected. Since the simulated un-orthorectified image and the image to be corrected are in the same image geometry after un-orthorectification, a simple affine transformation is sufficient for accurate image registration. The proposed method thus makes the transformation independent of the form and amount of geometric distortion. The image to be corrected is then spatially registered to the simulated un-orthorectified image and subsequently orthorectified using the DEM. We demonstrate the effectiveness of the proposed method by geometrically correcting a total of 100 HRCC multispectral images collectively acquired by CBERS-2 and CBERS-2B. The selected images are acquired over the Andes Mountains and the Amazon rainforest, two areas in South America representing vastly different landscapes. Quantitative evaluation of the method in geometrically
15
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
correcting CBERS-2 multispectral images results in an average Root Mean Square Error (RMSE) value of 0.85 pixel (17.04 m). Geometrically corrected CBERS-2B multispectral images have an average RMSE value of 0.82 pixel (16.34 m). The proposed method is fully automated and computationally efficient requiring less than 5 min for processing a CBERS-HRCC multispectral image on a desktop computing system with Windows 7 OS, Quad Core Intel 3.2 GHz processor, 12 GB memory, and a resident hard disk access. The paper is organized as follows. Section 2 describes the methodology and processing chain that allows for precise geo-referencing and orthorectification of CBERS-HRCC multispectral images. Section 3 presents the experimental results and Section 4 provides summary and conclusions. 2. Theory and methodology CBERS satellite imagery processed to L2 level of correction is distributed by Brazil’s INPE and can be accessed from http:// www.dgi.inpe.br/CDSR/. We selected images over two study sites in South America (Andes Mountains and Amazon rainforest) that represent vastly different landscapes. The study site in Andes Mountains is located at the border of Chile and Argentina extending between 23.12°S to 30.71°S and 62.21°W to 72.04°W. The landscape in Andes Mountains represents rugged mountainous topography and is dominated by many active volcanoes (e.g., Nevado Ojos del Salado and Llullaillaco) and associated landforms. The study site in Amazon is located in Brazil and extends between 0.52°N to 6.07°S and 50.23°W to 60.14°W. This study site is characterized as densely forested area that also includes the Amazon River and its tributaries. For each of the study sites we selected 25 images acquired by a particular CBERS satellite. Thus, we processed a total of 100 images collectively acquired by CBERS-2 and CBERS-2B. Acquisition dates for CBERS-2 and CBERS-2B images are provided in Table 1. The CBERS-HRCC images selected within the Andes study site have 10% or less cloud cover. Constant cloud cover in the Amazon rainforest, however, poses a challenge to obtain cloud-free
CBERS-HRCC images. Therefore, the CBERS-HRCC images selected within the Amazon study site contain 30% or less cloud cover. Corresponding Landsat TM image processed to L1T level acquired reasonably close to the date of the CBERS-HRCC image is selected based on the data quality and low cloud cover (610%) for both the study sites. The elevation information is obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) product distributed by METI and NASA. ASTER GDEM data are available on a 1 arc-second (approximately 30 m at the equator) grid and are referenced to the 1984 World Geodetic System (WGS84)/1996 Earth Gravitational Model (EGM96) geoid. DEM data is processed to create seamless elevation data by removing residual bad/void values. Both Landsat TM and ASTER GDEM can be accessed from http:// earthexplorer.usgs.gov/. An overview of steps involved in the proposed geometric correction methodology is presented in Fig. 2. The Landsat TM data processed to L1T level of correction is used as the reference image for geometric correction (geo-referencing and orthorectification) of the multispectral images from CBERS-HRCC sensor. A Landsat L1T image is precisely geo-referenced using ground control points and orthorectified by employing a Digital Elevation Model (DEM). The un-orthorectification procedure introduces geometric distortions (caused by topographic variations and satellite viewing perspectives) in the TM image by utilizing a DEM and CBERS-HRCC sensor parameters (satellite altitude and GSD). The resulting simulated un-orthorectified image is identical to the CBERS-HRCC multispectral image in terms of geometric distortions. A geo-referenced CBERS-HRCC image is then generated by spatially registering the CBERS-HRCC image to the simulated un-orthorectified image. Finally, the geo-referenced CBERS-HRCC image is orthorectified using a DEM and CBERS-HRCC sensor parameters. A detailed description of individual steps involved in the proposed method is presented below. The geometry of topographic distortion in nadir viewing satellite imagery is shown in Fig. 3a. Due to the local topographic variations, the terrain surface at point A, which is at a height h (above
Table 1 CBERS-2 and CBERS-2B Path/Row and acquisition dates. #
Path/Row Andes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
173/128 173/129 173/130 173/131 173/132 174/128 174/129 174/130 174/131 174/132 175/128 175/129 175/130 175/131 175/132 176/128 176/129 176/130 176/131 176/132 177/128 177/129 177/130 177/131 177/132
Acquisition date
Acquisition date
CBERS-2
CBERS-2B
07/21/2005 07/21/2005 07/21/2005 07/21/2005 07/21/2005 05/01/2005 05/01/2005 05/01/2005 05/01/2005 05/01/2005 12/18/2005 12/18/2005 12/18/2005 12/18/2005 12/18/2005 03/04/2005 03/04/2005 03/04/2005 03/04/2005 03/04/2005 03/27/2005 03/27/2005 03/27/2005 03/27/2005 03/27/2005
07/05/08 07/05/08 07/05/08 07/05/08 07/05/08 07/02/08 07/02/08 07/02/08 07/02/08 07/02/08 07/25/08 07/25/08 07/25/08 07/25/08 07/25/08 05/05/08 05/05/08 05/05/08 05/05/08 05/05/08 05/02/08 05/02/08 05/02/08 05/02/08 05/02/08
Path/Row Amazon
166/101 166/102 166/103 166/104 166/105 167/101 167/102 167/103 167/104 167/105 168/101 168/102 168/103 168/104 168/105 169/101 169/102 169/103 169/104 169/105 170/101 170/102 170/103 170/104 170/105
Acquisition date
Acquisition date
CBERS-2
CBERS-2B
10/28/05 10/28/05 07/16/05 07/16/05 07/16/05 09/29/05 09/03/05 08/08/05 08/08/05 07/13/05 08/31/05 08/31/05 08/31/05 08/31/05 08/31/05 09/23/05 09/23/05 09/23/05 09/23/05 09/23/05 08/25/05 08/25/05 08/25/05 08/25/05 08/25/05
09/16/08 06/30/08 06/30/08 08/21/08 08/21/08 11/30/08 11/30/08 08/18/08 11/30/08 08/18/08 09/10/08 11/01/08 08/15/08 06/24/08 06/24/08 08/12/08 09/07/08 12/20/08 07/17/08 07/17/08 07/14/08 08/09/08 07/14/08 07/14/08 07/14/08
16
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Fig. 2. Overview of the geometric correction methodology.
(a)
(b)
Fig. 3. (a) Geometry of topographic distortion in nadir viewing satellite imagery, and (b) illustration of image un-orthorectification process.
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
17
Fig. 4. True color composite images (Path-176/Row-130) acquired over the Andes Mountains showing the world’s highest active volcano (Nevado Ojos del Salado 27.11°S, 68.54°W) and the salt lake of Chile (Laguna Verde 26.90°S, 68.47°W). The images shown here are the spatial subset of (a) CBERS-2. (b) Landsat TM, and (c) geometrically corrected CBERS-2 image scenes. The spatial misalignment between TM and CBERS-2 before and after geometric correction is estimated to be 3,274.26 m and 18.01 m, respectively.
18
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Fig. 5. True color composite images (Path-176/Row-128) acquired over the Atacama Desert near the Andes Mountains showing Llullaillaco volcano located at 24.72°S, 68.54°W and Laguna de la Azufrera lake located at 25.08°S, 68.51°W. The images shown here are the spatial subset of (a) CBERS-2B, (b) Landsat TM, and (c) geometrically corrected CBERS-2B image scenes. The spatial misalignment between TM and CBERS-2B before and after geometric correction is estimated to be 2025.44 m and 13.45 m, respectively.
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
19
Fig. 6. False color composite images (Path-168/Row-105) acquired over the state of Pará in Brazil showing the Tapajós River which is a major tributary of the Amazon River and the ‘‘fishbone’’ pattern of deforestation along the highways BR163 and BR230 (below the river). The images shown here are the spatial subset of (a) CBERS-2, (b) Landsat TM, and (c) geometrically corrected CBERS-2 image scenes. The spatial misalignment between TM and CBERS-2 before and after geometric correction is estimated to be 10,477.90 m and 14.55 m, respectively.
20
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Fig. 7. False color composite images (Path-170/Row-102) acquired over the Nhamundá River which creates the border between the Brazilian states of Pará (north) and Amazonas (south). The images shown here are the spatial subset of (a) CBERS-2B, (b) Landsat TM, and (c) geometrically corrected CBERS-2B image scenes. The spatial misalignment between TM and CBERS-2B before and after geometric correction is estimated to be 2,018.02 m and 18.09 m, respectively.
21
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Table 2 RMSE statistics for CBERS-2 and CBERS-2B images before and after geometric correction. CBERS-2 RMSE (m)
CBERS-2B RMSE (m)
Before
After
Before
After
Andes Mean Std. dev. Max Min
6609.96 3364.54 10542.73 2081.73
16.94 1.48 18.97 14.23
3229.44 911.38 4132.60 2025.44
15.73 1.39 17.75 13.45
Amazon Mean Std. dev. Max Min
9405.60 2057.82 10876.56 3220.00
17.14 1.50 19.21 14.26
2154.85 1414.00 5531.73 377.36
16.95 1.48 18.82 13.61
1 ðRe þ HÞ sinðb þ aÞ ðb þ aÞ c Re dS ¼ sin Re
ð1Þ
where
8 > <
9 h i > = ðRe þ HÞ sinðbÞ 1 ReRþh e qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi a ¼ tan1 > :ðRe þ hÞ 1 ½ðRe þHÞ sinðbÞ2 ðRe þ HÞ cosðbÞ> ; Re
ð2Þ
and Fig. 8. Geometric accuracy of CBERS-2 and CBERS-2B HRCC images acquired over the Andes Mountains (a) before geometric correction, and (b) after geometric correction.
2 1
b ¼ sin
3
Re sinðcÞ 6 7 4qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi5: 2 2 Re þ ðRe þ HÞ 2Re ðRe þ HÞ cosðcÞ
ð3Þ
The calculation of relief displacement in Eq. (1) requires the knowledge of (i) satellite altitude (H) above the reference datum, (ii) arithmetic mean radius of earth (Re ) for the reference datum, and (iii) the terrain elevation (h) above the reference datum at a given location estimated from a DEM. A detailed description of the sensor geometric model and mathematical derivations required for estimating relief displacement dS is provided in Gao et al. (2009) and Shah (2010). As the first step, in order to un-orthorectify an image, the image pixel corresponding to ground location A þ dS(A0 ) is assigned the value of an image pixel corresponding to ground location A. The un-orthorectification process can be explained using Fig. 3b, where ðx1 ; y1 Þ; ðx2 ; y2 Þ; ðx3 ; y3 Þandðx4 ; y4 Þ correspond to the image corners. The start ðx12 ; y12 Þ and end ðx34 ; y34 Þ co-ordinates of the orbital track are the mid-points of the upper and lower image corners, respectively. If the orbital track on image is defined by the generalized line equation Ax þ By þ C ¼ 0, then the orientation of the orbital track with respect to the vertical line (North) is calculated as
2 1
h ¼ sin
3
jx12 x34 j 6 7 4qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi5: 2 2 ðx12 x34 Þ þ ðy12 y34 Þ
ð4Þ
Thus, if ds is the pixel unit displacement of ðxo ; yo Þ along the scanline in the image, then the pixel co-ordinates of the displaced location in the image are estimated as
xu ¼ xo þ k ds cosðhÞ Fig. 9. Geometric accuracy of CBERS-2 and CBERS-2B HRCC images acquired over the Amazon rainforest (a) before geometric correction, and (b) after geometric correction.
where
0
the reference datum), is imaged at point A . This results in a relief displacement of dS units on ground (Fig. 3a), which is calculated as follows (Shah, 2010),
ð5Þ
yu ¼ yo þ k ds sinðhÞ
k¼
8 > < 1; > : þ1;
Axo þByo þC
pffiffiffiffiffiffiffiffiffiffi P 0 2 2 A þB
Axo þByo þC
pffiffiffiffiffiffiffiffiffiffi < 0 2 2 A þB
:
ð6Þ
22
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Fig. 10. Geometrically corrected CBERS images acquired over (a) Amazon, and (b) Andes Mountains, overlaid on a Landsat TM mosaic. The high geometric accuracies attained using our method is indicated by the perfect alignment at the CBERS-TM image boundaries resulting in spatial continuity of different terrain features.
In Eqs. (5) and (6), ðxo ; yo Þ correspond to the image co-ordinates of an orthorectified pixel, whereas ðxu ; yu Þ denote its un-orthorectified location in image co-ordinates. The pixel unit displacement ds in Eq. (5) is calculated from the corresponding ground relief displacement (dS) determined in Eq. (1) as
dS ds ¼ GSD
ð7Þ
The next step comprises of geo-referencing the CBERS-HRCC image by registering it with the un-orthorectified image simulated using the TM image. This is accomplished by identifying a set of control points from the image pair using an automated image matching technique. There exist several methods for automated selection of control points (e.g., Gianinetto and Scaioni, 2008; Kennedy and Cohen, 2003; Liu and Chen, 2009; Mikolajczyk and Schmid, 2005). One might make use of any commercial image analysis software or develop their own method for the automated selection of control points. We selected the control points using the automated area-based matching method available in ENVIÒ. For this method, we chose the Förstner interest point operator, a minimum normalized cross correlation of 0.7, a moving window size of 11 pixels, an area chip size of 256 pixels, and a search window size of 101 pixels. The extracted control points are then used to estimate the parameters of a 2D affine transformation function for registration. Since the proposed method makes the two images geometrically alike prior to registration, a simple global transformation is sufficient to accurately model the spatial misalignment. As the final step, geometric distortions caused by topographic variations and satellite viewing perspectives are removed by
orthorectifying the geo-referenced CBERS-HRCC image. For a given pixel at location ðxu ; yu Þ in the geo-referenced image, its corresponding orthorectified pixel location is calculated as
xo ¼ xu k ds cosðhÞ yo ¼ yu k ds sinðhÞ
ð8Þ
DEM and CBERS-HRCC sensor parameters are used in calculating the relief displacement dS (Eq. (1)) and the corresponding pixel unit displacement ds in the image is calculated using Eq. (7). The HRCC instrument on-board CBERS-2 and CBERS-2B acquires images with 113 km swath width at 20 m GSD while orbiting at an altitude of 778 km above the reference datum. The Landsat TM images distributed by the USGS have a GSD of 30 m and 180 km swath width. Before simulating the un-orthorectified image, the Landsat TM image is resampled to 20 m through a bilinear interpolation to match the CBERS-HRCC image GSD. Also, four or more TM images are mosaicked to ensure complete overlap with a CBERSHRCC scene. In order to create a ‘‘radiometrically’’ seamless TM mosaic, the TM image DN (digital number) values are converted to exo-atmospheric reflectance (Schott, 2007). This process of converting the DN values to spectral radiance and then normalizing for the solar irradiance reduces the inter-scene variability. However, in cases when the calibration coefficients for radiance conversion are not available, DN values can be directly used in the TM mosaic generation process. Finally, the TM mosaic is projected on the UTM zone determined by the CBERS-HRCC scene center. In many cases, the CBERS-HRCC images are misaligned with respect to the TM image by more than a kilometer. Hence, prior to un-orthorectification,
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
a coarse spatial alignment is performed between the TM mosaic and the CBERS-HRCC image using a Fast Fourier Transform (FFT)based image matching method (e.g., Kuglin and Hines, 1975). In most cases, a simple translation along x and/or y axis is sufficient to approximately align the image with the resampled TM mosaic. The main purpose of this coarse image registration is to ensure that the Landsat TM mosaic roughly covers the same scene on the ground as that covered by the CBERS-HRCC image. To geometrically correct the multispectral bands, the near infra-red spectral band (0.77–0.89 lm in CBERS-HRCC and 0.77– 0.90 lm in TM) is used for coarse image registration as well as for estimating the parameters of 2D affine transformation function in the geo-referencing step. However, one can use any spectral band that is common to both CBERS-HRCC and TM for this purpose. Since all the spectral bands on HRCC are accurately co-registered with each other, the parameters estimated using the near infra-red spectral band are subsequently used to geometrically correct all other (blue: 0.45–0.52 lm, green: 0.52–0.59 lm, and red: 0.63–0.69 lm) CBERS-HRCC multispectral bands.
3. Experimental results The performance of the proposed method in geometrically correcting the CBERS-HRCC multispectral images is evaluated using images acquired over two selected study sites in South America (Andes Mountains and Amazon rainforest) that represent vastly different landscapes. Figs. 4–7 exhibit the performance of the proposed method in geometrically correcting the multispectral bands acquired by the HRCC sensor on-board CBERS-2 and CBERS-2B satellites. In these illustrations, true color composite images are shown for the Andes study site (Figs. 4 and 5) and false color (color infra-red) composite images are shown for the Amazon study site (Figs. 6 and 7). A significant amount of spatial misalignment between Landsat TM and the CBERS images prior to geometric correction is evident from these figures. Accurate geo-referencing and orthorectification of the multispectral bands from the CBERS-HRCC sensor using the proposed method result in images with sub-pixel accuracies. The plots in Figs. 8 (Andes) and 9 (Amazon) provide the calculated spatial alignment errors (RMSE) between Landsat TM and the CBERS-HRCC images before and after geometric correction. The RMSE for each of the 100 images is calculated using a set of 30 ground control points that are fairly evenly distributed throughout the image. These control points were manually selected by identifying prominent surface features that were common between the image pair. The large values of RMSE before correction can be attributed to the large geo-referencing inaccuracies in the CBERS images. The results presented in Figs. 8 and 9 indicate that the RMSE values of the corrected CBERS images have significantly decreased, resulting in sub-pixel accuracies. Table 2 presents the spatial alignment error (RMSE) statistics for the CBERS images before and after geometric correction. The mean and standard deviation values of RMSE are much lower for the geometrically corrected CBERS images. Also, the maximum RMSE values (<20 m) after geometric correction confirm the robustness of the method in geometrically correcting the images over dynamic landscapes. Fig. 10(a) and (b) corresponds to the geometrically corrected CBERS images overlaid on the Landsat TM mosaic. Spatial continuity along the image boundary (when compared with Fig. 1) demonstrates perfect spatial alignment between the corrected CBERS-HRCC multispectral and the L1T Landsat TM images. This further confirms the high geometric accuracies in CBERS-2 and CBERS-2B HRCC multispectral imagery attained using the proposed method.
23
4. Summary and conclusions CBERS satellite imagery is identified as one of the potential data sources for monitoring Earth surface dynamics in the event of a Landsat data gap. Currently available multispectral imagery from the HRCC instrument on-board CBERS satellites (CBERS-2 and CBERS-2B) are processed to L2 level of correction by the DGI. CBERS images processed to L2 level are not precisely geo-referenced and orthorectified. CBERS-HRCC image product accuracy is expected to be in the order of 2–11 km. Therefore, the use of CBERS-HRCC multispectral images to monitor Earth surface changes necessitates accurate geometric correction of these images. In this paper we presented a novel method for automatic geo-referencing and orthorectification of CBERS-HRCC multispectral images. The method is used for geometric correction of CBERS-HRCC images using a Landsat TM image as the reference image. The novelty of our method is that it introduces geometric distortions, caused by topographic variations and satellite viewing perspectives, in the reference image prior to registering it with the CBERS-HRCC image. Since the simulated un-orthorectified image and the image to be corrected are in the same image geometry after un-orthorectification, a simple affine transformation is sufficient for accurate image registration. Thus, unlike the existing geometric correction methods, our method makes the image transformation independent of the form and amount of geometric distortion. Therefore, the significance of our method is that the user does not need to experiment with different transformation functions to identify the one that yields the best geometric correction result. The robustness of the proposed method in geometrically correcting the images over dynamic landscapes is demonstrated using CBERS-2 and CBERS-2B images acquired over the Amazon rainforests and Andes mountain range. Geometrically corrected CBERS-2 and CBERS-2B HRCC images have an average RMSE value of 0.85 pixel and 0.82 pixel, respectively. These low average RMSE values of the CBERS-HRCC images indicate the high geometric accuracy attained using the method presented in this paper. While the applicability of this method is demonstrated here using selected CBERS images, it has potential for accurate geometric correction of the entire archive of CBERS-HRCC multispectral images. Acknowledgment The authors would like to thank the anonymous reviewers for their constructive comments on this manuscript. References Collins, S.H., 1968. Stereoscopic orthophoto maps. The canadian, surveyor 22, 167– 176. Gao, F., Masek, J.G., Wolfe, R.E., 2009. Automated registration and orthorectification package for landsat and landsat-like data processing. J. Appl. Rem. Sens. 3, 033515–033535. Ghoshtasby, A., 1998. Image registration by local approximation. Image Vision Comput. 6, 255–261. Gianinetto, M., Scaioni, M., 2008. Automated geometric correction of highresolution pushbroom satellite data. Photogram. Eng. Rem. Sens. 74, 107–116. INPE (2004). Geometric quality assessment of CBERS-2. (
24
C. Devaraj, C.A. Shah / ISPRS Journal of Photogrammetry and Remote Sensing 89 (2014) 13–24
Powell, S.L., Pflugmacher, D., Kirschbaum, A.A., Kim, Y., Cohen, W.B., 2007. Moderate resolution remote sensing alternatives: a review of landsat-like sensors and their applications. J. Appl. Rem. Sens. 1, 1–16. Sausen, T.M., 2001. The China–Brazil Earth Resources Satellite (CBERS). ISPRS Soc. 6, 27–28. Schott, J.R., 2007. Remote Sensing: The Image Chain Approach, second ed. Oxford University Press. Sertel, E., Kutoglu, S.H., Kaya, S., 2007. Geometric correction accuracy of different satellite sensor images: application of figure condition. Int. J. Rem. Sens. 28, 4685–4692. Shah, C.A. (2010). Assessment of high-latitude arctic lake dynamics in representative areas on continuous permafrost. Ph.D. Dissertation, Dept. Geography, University of California, Los Angeles (UCLA).
Slonecker, E.T., Johnson, B., McMahon, J., 2009. Automated imagery orthorectification pilot. J. Appl. Rem. Sens. 3, 033552–033568. Smith, D.P., Atkinson, S.F., 2001. Accuracy of rectification using topographic map versus GPS ground control points. Photogram. Eng. Rem. Sens. 67, 565–570. Tao, C.V., Hu, Y., 2001. A comprehensive study of the rational function model for photogrammetric processing. Photogram. Eng. Rem. Sens. 67, 1347–1357. Tominaga, J., Ferreira, M., 2008. Payload operations of CBERS satellite series by INPE. Space Oper. Commun. 5, 19–23. Toutin, T., 2004. Geometric processing of remote sensing images: models, algorithms and methods. Int. J. Rem. Sens. 25, 1893–1924. USGS 2007. Landsat Data Gap Study. Technical Report: DCN Version 1.0.