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Introduction
Landuse and Landcover mapping using remotely sensed image data involves delineation of the constituent units. Such a delineation, however, is dependent on the wavelength region in which they are imaged and also the spatial resolution of the imaging sensor. It is well known that the visible and near infrared bands provide information on the common land cover features and such as water, soil and vegetation. But the spectral signatures of soil and rock units suggest that the SWIR region would give better information about them Goward (1994). The limitation in using the SWIR band is that only very low spatial resolution images could be obtained (viz. 70m spatial resolution of IRS 1C band 4 ). One way of obtaining better information about soil and rock units, apart from the water and vegetation features, is to use the information obtained from both SWIR and VNIR bands.
Multisensor image fusion is one of the techniques that has proved its potential in bringing out maximum information about most of the landcover features. Image fusion may be defined as a formal frame work in which are expressed means and tools for the alliance of data originating from different sources. It aims at obtaining information of greater quality; the exact definition of greater quality will depend upon application.” Wald (1999). The fused image should have more complete information that is more useful for human or machine perception. The common objectives of fusion are (i) to extract all the useful information from the source images (ii) to avoid artifacts or inconsistencies, which will distract human observers or the following processing and (iii) to be reliable and robust to imperfection such as misregistration. Image fusion improves the interpretability of satellite images. Image data obtained from different types of sensors provide complementary information about a scene. Image data fusion helps us to extract maximum information from the data set in such a way as to achieve optimal resolution in the spatial and spectral domain. The detection and the recognition of objects in the scene will be done with minimum error probability where the redundant information will improve reliability, and the complementary information will improve capability.
Fig 1: VINR
This paper presents the results of an exercise that involved fusion of VNIR and SWIR images of an area in south India containing the commonly observed land cover features such as water, vegetation (agriculture and forest), various rock types, soil types and built environments. The aim of image fusion here is to integrate complementary data (in VNIR & SWIR) in order to obtain more information than cannot be derived from single sensor data alone.
This is an unique attempt in the sense that this is probably haps the first study in India that has attempted to use the image data obtained from the ASTER sensor.
Fig 2: SWIR
Image data and study area
The digital image data used in the study has been obtained by the ASTER sensor. ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is an imaging instrument on board TERRA -1, a satellite launched in December 1999 as part of NASA’s Earth Observing System (EOS). ASTER is used to obtain detailed maps of land surface temperature, emissivity, reflectance and elevation and is a suite of three high-performance optical radiometers with 14 spectral channels (Table.1), that contribute valuable scientific and operational data on the earth. It is designed to meet the mission requirements of operational users and scientific researchers in the visible and near infrared (VNIR) the short wavelength infrared (SWIR) and the thermal infrared (TIR).
The major features of ASTER are : (i) Simultaneous earth surface images from the visible to the thermal infrared, (ii) Higher geometric and radiometric resolution in each band than current satellite sensors, (iii) Near infrared stereoscopic image pairs collected during the same orbit, (iv) Exquisite optics that allow the instrument axis to move as much as +- 24 degrees for SWIR and TIR cross-talk direction from the nadir, and (v) Highly reliable cryocoolers for the SWIR and TIR sensors
Fig 3(a): Fused Image PCA
Visible and Near infrared Radiometer (VNIR)
The VNIR high resolution radiometer observes the targets using solar radiation reflected from the earth surfaces in three visible and near infrared bands (Table.1). Its main objectives are land survey, vegetation assessment, environmental protection and disaster prevention. VNIR data can be combined with data from SWIR and TIR to provide synergistic interpretation.
Fig 3(b): Fused Image through Brovey transformation method
The stereoscopic image sensor views 27.6 degrees backward of band 3 sensor in the same orbit. VNIR degrees in cross track from nadir direction. Stereoscopic observation capability is useful for geomorphology and creation of DEM. VNIR automatically corrects on board for the geometric aberration between the backward field of view and the nadir FOV caused by the rotation of the earth.
Each band has a linear charge coupled device (CCD) array that sweeps 60Km swath over the earth’s surface in a bush broom manner (Fig. 1).
Fig 3(c): Fused Image IHS
Short wavelength Infrared Radiometer (SWIR)
SWIR is an advanced high resolution multispectral radiometer which detects reflected solar radiation from the earth surfaces in the wavelength region of 1.6 – 2.43 micrometer. SWIR is especially advantageous for resources discriminations such as rocks and minerals and for environmental survey such as vegetations and volcanoes. The detector consists of six band PTSI Schottky barrier type CCDs integrated on a chip and each band row has 2048 effective pixels. Spectral ramifications are performed through stripe shaped band pass filters placed on the chip (ERSDAC, 2000) (Fig. 2).
Fig 4(a): Classified Images of VNIR
Study site
The site chosen for the study is part of mining district of Salem, Tamil nadu, south India. The area is characterised by the presence of large hillocks which are part of western ghats region, dense and sparse forest, agriculture areas, open cast mines, urbanized areas and water bodies (lakes and tanks). This area was chosen for this study as it provides an opportunity to asses the potential of image fusion techniques and also the capabilities of the imaging sensor to delineate and map most of the common land cover categories.
Methodology
Since this study aims to extract enhanced information about landcover features by fusion of SWIR & VNIR images, the methodology used and described and used is about the various fusion techniques and the visual description of the resulting images. The frequently used methods of image fusion include the PCA method, IHS method and Brovey transformation method. Following is a brief explanation of these methods used in this study. A detailed description of the techniques is given in Vani 1999 and Pohl 1996.
Fig 4(b): Classified Images of ASTER
IHS method
This is a commonly used method wherein the three bands of lower spatial resolution data are transformed to the IHS space. Intensity (I) refers the total brightness of the colour, hue (H) to the dominant or average wavelength of the light contributing to the colour and saturation (S) o the purity of colour. The IHS transformation separates spatial (I) and spectral (H,S) information from a standard RGB image. The stretched higher spatial resolution image replaces the intensity component image and hue and saturation components are over sampled to higher resolution before the images are re-transformed back to the original space (Fig. 3c).
PCA Method
The PCA (Principal Componant Analysis) method is much similar to the IHS method. The different bands of the multispectral data are used as input to the principal component analysis procedure. All the spectral bands of the image are simplified into principal component axes. PCA removes the redundancy of information contents. Chavez et al. 1991 observe less spectral distortion in PCA method compared to IHS method because the principal component image is more correlated to the higher resolution image (Fig. 3a).
Brovey transformation method
The Brovey transformation is a special arithmetic combination including ratio. It normalizes the multi-spectral bands used for an RGB display and multiplies the result by any other higher resolution image to add the intensity or brightness components to the image. Pohl 1999 gives a detailed description of this technique (Fig. 3b)
Results and Discussion
As this study aims to evaluate the applicability of fusion of the VNIR and SWIR image data for improved land cover information extraction, the results of the fusion is discussed in the following section.
Comparison of raw (VNIR) and fused (VNIR + SWIR) images
As mentioned in section 1.0 fusion of multi sensor images would certainly enhance the landcover features and we could derive better information about them. Accordingly, the VNIR and SWIR images of salem area obtained by ASTER were fused using the PCA, IHS and Brovey transformation as mentioned in section 3.0.
The results of fusion indicate enhanced display of most of the soil and rocky features, mainly due to complementary information offered by the SWIR band of ASTER. How ever, each fusion technique tends to enhance only certain features. Hence a comparison of results of all techniques was attempted in this study. A comparison of the outputs of various fusion techniques indicates that PCA method of image fusion enhances most of the feature in best possible manner. This is followed by Brovey and then IHS technique. The reason for such a difference is that in PCA technique the first principal component, which contains maximum information content, is retained in the fused image where as, it is not so with other techniques. Further details about the comparison of various fusion techniques may be found in Vani (2001), Chavez (1991) and Lakshmi (2000). Since we are convinced to a great extent that PCA method provides good results, all further discussions in this paper would be based on the results of PCA technique of fusion.
In general, it is observed that the SWIR image provides information about rock and soil features better than VNIR images, due to characteristics reflectance’s of rock an soils in the 1.8 – 2.5 mm region. However the coarser spatial resolution (30m) is a limiting factor and finer details of the soil and rock features are not displayed in the image. To overcome this limitations, and to enhance the soil and rocky features, in addition to the other land cover features (water, vegetation, built-up environment), fusion of VNIR (15m resolution) SWIR (30m resolution) was attempted.
Fusion of ASTER VNIR & SWIR image has resulted in (I) Improved contrast between urban, soil and rocky features (ii) enhanced appearance of rock outcrops that are covered with vegetation (iii) Enhanced appearance of roads (iv)Delineation of vegetated rocky areas and forested areas.
Detailed inspection of the fused images reveals that the major categories of rocks has been very well demarcated in comparison to the original VNIR & SWIR images. The individual outcrop boundaries have been distinctly displayed due to the improved spatial resolution and spectral content in the fused images. Thus pockets of charnokite, gabbro and pneiss are very well represented in the fused images, leading to improved information extraction about these features.
The barren patches of soil are represented with better contrast in the fuse images due to increased reflectence of soil in the SWIR region compared to VNIR region.
Vegetated features such as plantation, agricultural areas, dense and moderately dense forest, are better represented due to fusion with SWIR data.
Because of the relatively higher reflectance of water in visible region and almost total absorbtion in the SWIR region, enough contrast between water and land features is provided in the fused images.
Having observed that multi sensor fusion of ASTER image data enhances the land cover features, an attempt was made to prepare landuse/landcover map by classification of fused image data.
This exercise was attempted despite the fact that many studies have demonstrated that classification of fused images does not result in appreciable increase in accuracy and that certain features tend to get disturbed in terms of their spectral content Musa (2000). Apart from these limitations, synthetic pixels/artifacts are generated Schowengerdt (1997)
Supervised classification of VNIR, SWIR and fused images was attempted using training sets that were common to VNIR and fused images. A separate training set was generated for SWIR image because of its coarser resolution. The training sites comprised of classes belonging to water bodies, agricultural vegetation, forested vegetation, built-up area, four different rock types and mined area. The sites were selected using a region growing algorithm to avoid error in choice of pixels for training class. The number of pixels in a training site for a given class was decided based on the proportional representation of the class. Care was also taken to adhere to the rule of 3N pixels per training class where N represents the number of bands.
Classification of visible and NIR resulted in a map (fig.4) that showed the eight land cover classes, while water body and vegetation classes were displayed without much misclassification, the other categories of classes such as urban/built-up and rock types were displayed with some amount of misclassification. This is perhaps due to the lower signature separability of urban and rocky classes in the VNIR bands. The overall accuracy and kappa coefficient values are 58.84 % and 0.54 respectively. Such a low accuracy is due to the mis-classification of the rock type and urban classes in the VNIR image data. The overall accuracy and kappa statistics for SWIR are 53% and 0.47 respectively. This is mainly due to larger inaccuracies contributed by water and vegetation classes and perhaps due to the lower spatial resolution (30m) (Fig. 4a-4b).
Since lower accuracy was obtained while classifying the VNIR and SWIR images, classification of fused images was attempted. The fused image was obtained by merging VNIR (15m resolution) and SWIR (30m resolution) bands to result in a 18m VNIR + SWIR images with 15m resolution. Classification was attempted on this image using the same training sites as that for VNIR image. The resulting classified image proved to be better than the classified image obtained from VNIR image. The four rock types were represented with greater accuracy in the same manner as water body and vegetation classes. Improved accuracy may be attributed to the complementary information provided by VNIR and SWIR images. While the information from SWIR image contributed to differentiate between rock types, information from the visible and NIR bands contributed to distinguish between waterbodies and vegetation classes. Thus an overall accuracy of 66% and kappa value of 0.61 was obtained .
In this study, classification was attempted not as a tool to evaluate the quality of the fusion. However, the training sites used for classification of VNIR image were also used for classification of the fused image. This was done to verify the accuracy of classification of fused images. It was found that accuracy of classification improves on fusion of images. The classification exercise was performed not to achieve very high levels of accuracy but to demonstrate that classification accuracy improves in fused images.
Conclusion
This study has evaluated the potential of ASTER VNIR and SWIR sensors for landuse / landcover mapping. The capabilities of individual sensors and the fused outputs were evaluated by visual observation and by image classification.
While the individual VNIR and SWIR images provide little information about land cover classes, it was observed that fusion of images obtained from these two sensors resulted in enhancement of land cover features for improved mapping of the same. Information in the VNIR image contributed to the enhancement of vegetation and water classes. Rock and soil units were enhanced due to the contribution by the information in the SWIR images. Improvement in spatial resolution of the SWIR images and the contribution of complementary information present in the SWIR images resulted in enhancement of soil and rock units. Improved contrast between waterbodies, vegetation and rockunits was also observed. Thus the potential of ASTER data and fusion of the VNIR and SWIR images for enhanced landcover mapping has been demonstrated in this paper. Further study is needed in the area of classification of fused ASTER images.
Acknowledgement
The authors thank the ERSDAC (Earth Remote Sensing Data Analysis Center), Japan for providing the ASTER images to them for ARO on ASTER data use. The rights on image data belong to the Ministry of Economy, Trade and Industry, Japan. Institute of Remote Sensing and Centre for Geoscience and Engineering, Anna University are thanked by KV for providing the necessary support for this research.
References
Fusion of ASTER image data for enhanced mapping of landcover features
K. Vani
Institute of Remote Sensing
Anna University, Chennai
vani@annauniv.edu
S. Sanjeevi
Center for Geoscience and Engineering, Anna University, Chennai
ssanjeevi@annauniv.edu
K. Vani
Institute of Remote Sensing
Anna University, Chennai
vani@annauniv.edu
S. Sanjeevi
Center for Geoscience and Engineering, Anna University, Chennai
ssanjeevi@annauniv.edu
Introduction
Landuse and Landcover mapping using remotely sensed image data involves delineation of the constituent units. Such a delineation, however, is dependent on the wavelength region in which they are imaged and also the spatial resolution of the imaging sensor. It is well known that the visible and near infrared bands provide information on the common land cover features and such as water, soil and vegetation. But the spectral signatures of soil and rock units suggest that the SWIR region would give better information about them Goward (1994). The limitation in using the SWIR band is that only very low spatial resolution images could be obtained (viz. 70m spatial resolution of IRS 1C band 4 ). One way of obtaining better information about soil and rock units, apart from the water and vegetation features, is to use the information obtained from both SWIR and VNIR bands.
Multisensor image fusion is one of the techniques that has proved its potential in bringing out maximum information about most of the landcover features. Image fusion may be defined as a formal frame work in which are expressed means and tools for the alliance of data originating from different sources. It aims at obtaining information of greater quality; the exact definition of greater quality will depend upon application.” Wald (1999). The fused image should have more complete information that is more useful for human or machine perception. The common objectives of fusion are (i) to extract all the useful information from the source images (ii) to avoid artifacts or inconsistencies, which will distract human observers or the following processing and (iii) to be reliable and robust to imperfection such as misregistration. Image fusion improves the interpretability of satellite images. Image data obtained from different types of sensors provide complementary information about a scene. Image data fusion helps us to extract maximum information from the data set in such a way as to achieve optimal resolution in the spatial and spectral domain. The detection and the recognition of objects in the scene will be done with minimum error probability where the redundant information will improve reliability, and the complementary information will improve capability.
Fig 1: VINR
This paper presents the results of an exercise that involved fusion of VNIR and SWIR images of an area in south India containing the commonly observed land cover features such as water, vegetation (agriculture and forest), various rock types, soil types and built environments. The aim of image fusion here is to integrate complementary data (in VNIR & SWIR) in order to obtain more information than cannot be derived from single sensor data alone.
This is an unique attempt in the sense that this is probably haps the first study in India that has attempted to use the image data obtained from the ASTER sensor.
Fig 2: SWIR
Image data and study area
The digital image data used in the study has been obtained by the ASTER sensor. ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is an imaging instrument on board TERRA -1, a satellite launched in December 1999 as part of NASA’s Earth Observing System (EOS). ASTER is used to obtain detailed maps of land surface temperature, emissivity, reflectance and elevation and is a suite of three high-performance optical radiometers with 14 spectral channels (Table.1), that contribute valuable scientific and operational data on the earth. It is designed to meet the mission requirements of operational users and scientific researchers in the visible and near infrared (VNIR) the short wavelength infrared (SWIR) and the thermal infrared (TIR).
The major features of ASTER are : (i) Simultaneous earth surface images from the visible to the thermal infrared, (ii) Higher geometric and radiometric resolution in each band than current satellite sensors, (iii) Near infrared stereoscopic image pairs collected during the same orbit, (iv) Exquisite optics that allow the instrument axis to move as much as +- 24 degrees for SWIR and TIR cross-talk direction from the nadir, and (v) Highly reliable cryocoolers for the SWIR and TIR sensors
Fig 3(a): Fused Image PCA
Visible and Near infrared Radiometer (VNIR)
The VNIR high resolution radiometer observes the targets using solar radiation reflected from the earth surfaces in three visible and near infrared bands (Table.1). Its main objectives are land survey, vegetation assessment, environmental protection and disaster prevention. VNIR data can be combined with data from SWIR and TIR to provide synergistic interpretation.
Fig 3(b): Fused Image through Brovey transformation method
The stereoscopic image sensor views 27.6 degrees backward of band 3 sensor in the same orbit. VNIR degrees in cross track from nadir direction. Stereoscopic observation capability is useful for geomorphology and creation of DEM. VNIR automatically corrects on board for the geometric aberration between the backward field of view and the nadir FOV caused by the rotation of the earth.
Each band has a linear charge coupled device (CCD) array that sweeps 60Km swath over the earth’s surface in a bush broom manner (Fig. 1).
Fig 3(c): Fused Image IHS
Short wavelength Infrared Radiometer (SWIR)
SWIR is an advanced high resolution multispectral radiometer which detects reflected solar radiation from the earth surfaces in the wavelength region of 1.6 – 2.43 micrometer. SWIR is especially advantageous for resources discriminations such as rocks and minerals and for environmental survey such as vegetations and volcanoes. The detector consists of six band PTSI Schottky barrier type CCDs integrated on a chip and each band row has 2048 effective pixels. Spectral ramifications are performed through stripe shaped band pass filters placed on the chip (ERSDAC, 2000) (Fig. 2).
Fig 4(a): Classified Images of VNIR
Study site
The site chosen for the study is part of mining district of Salem, Tamil nadu, south India. The area is characterised by the presence of large hillocks which are part of western ghats region, dense and sparse forest, agriculture areas, open cast mines, urbanized areas and water bodies (lakes and tanks). This area was chosen for this study as it provides an opportunity to asses the potential of image fusion techniques and also the capabilities of the imaging sensor to delineate and map most of the common land cover categories.
Methodology
Since this study aims to extract enhanced information about landcover features by fusion of SWIR & VNIR images, the methodology used and described and used is about the various fusion techniques and the visual description of the resulting images. The frequently used methods of image fusion include the PCA method, IHS method and Brovey transformation method. Following is a brief explanation of these methods used in this study. A detailed description of the techniques is given in Vani 1999 and Pohl 1996.
Fig 4(b): Classified Images of ASTER
IHS method
This is a commonly used method wherein the three bands of lower spatial resolution data are transformed to the IHS space. Intensity (I) refers the total brightness of the colour, hue (H) to the dominant or average wavelength of the light contributing to the colour and saturation (S) o the purity of colour. The IHS transformation separates spatial (I) and spectral (H,S) information from a standard RGB image. The stretched higher spatial resolution image replaces the intensity component image and hue and saturation components are over sampled to higher resolution before the images are re-transformed back to the original space (Fig. 3c).
PCA Method
The PCA (Principal Componant Analysis) method is much similar to the IHS method. The different bands of the multispectral data are used as input to the principal component analysis procedure. All the spectral bands of the image are simplified into principal component axes. PCA removes the redundancy of information contents. Chavez et al. 1991 observe less spectral distortion in PCA method compared to IHS method because the principal component image is more correlated to the higher resolution image (Fig. 3a).
Brovey transformation method
The Brovey transformation is a special arithmetic combination including ratio. It normalizes the multi-spectral bands used for an RGB display and multiplies the result by any other higher resolution image to add the intensity or brightness components to the image. Pohl 1999 gives a detailed description of this technique (Fig. 3b)
Results and Discussion
As this study aims to evaluate the applicability of fusion of the VNIR and SWIR image data for improved land cover information extraction, the results of the fusion is discussed in the following section.
Comparison of raw (VNIR) and fused (VNIR + SWIR) images
As mentioned in section 1.0 fusion of multi sensor images would certainly enhance the landcover features and we could derive better information about them. Accordingly, the VNIR and SWIR images of salem area obtained by ASTER were fused using the PCA, IHS and Brovey transformation as mentioned in section 3.0.
The results of fusion indicate enhanced display of most of the soil and rocky features, mainly due to complementary information offered by the SWIR band of ASTER. How ever, each fusion technique tends to enhance only certain features. Hence a comparison of results of all techniques was attempted in this study. A comparison of the outputs of various fusion techniques indicates that PCA method of image fusion enhances most of the feature in best possible manner. This is followed by Brovey and then IHS technique. The reason for such a difference is that in PCA technique the first principal component, which contains maximum information content, is retained in the fused image where as, it is not so with other techniques. Further details about the comparison of various fusion techniques may be found in Vani (2001), Chavez (1991) and Lakshmi (2000). Since we are convinced to a great extent that PCA method provides good results, all further discussions in this paper would be based on the results of PCA technique of fusion.
In general, it is observed that the SWIR image provides information about rock and soil features better than VNIR images, due to characteristics reflectance’s of rock an soils in the 1.8 – 2.5 mm region. However the coarser spatial resolution (30m) is a limiting factor and finer details of the soil and rock features are not displayed in the image. To overcome this limitations, and to enhance the soil and rocky features, in addition to the other land cover features (water, vegetation, built-up environment), fusion of VNIR (15m resolution) SWIR (30m resolution) was attempted.
Fusion of ASTER VNIR & SWIR image has resulted in (I) Improved contrast between urban, soil and rocky features (ii) enhanced appearance of rock outcrops that are covered with vegetation (iii) Enhanced appearance of roads (iv)Delineation of vegetated rocky areas and forested areas.
Detailed inspection of the fused images reveals that the major categories of rocks has been very well demarcated in comparison to the original VNIR & SWIR images. The individual outcrop boundaries have been distinctly displayed due to the improved spatial resolution and spectral content in the fused images. Thus pockets of charnokite, gabbro and pneiss are very well represented in the fused images, leading to improved information extraction about these features.
The barren patches of soil are represented with better contrast in the fuse images due to increased reflectence of soil in the SWIR region compared to VNIR region.
Vegetated features such as plantation, agricultural areas, dense and moderately dense forest, are better represented due to fusion with SWIR data.
Because of the relatively higher reflectance of water in visible region and almost total absorbtion in the SWIR region, enough contrast between water and land features is provided in the fused images.
Having observed that multi sensor fusion of ASTER image data enhances the land cover features, an attempt was made to prepare landuse/landcover map by classification of fused image data.
This exercise was attempted despite the fact that many studies have demonstrated that classification of fused images does not result in appreciable increase in accuracy and that certain features tend to get disturbed in terms of their spectral content Musa (2000). Apart from these limitations, synthetic pixels/artifacts are generated Schowengerdt (1997)
Supervised classification of VNIR, SWIR and fused images was attempted using training sets that were common to VNIR and fused images. A separate training set was generated for SWIR image because of its coarser resolution. The training sites comprised of classes belonging to water bodies, agricultural vegetation, forested vegetation, built-up area, four different rock types and mined area. The sites were selected using a region growing algorithm to avoid error in choice of pixels for training class. The number of pixels in a training site for a given class was decided based on the proportional representation of the class. Care was also taken to adhere to the rule of 3N pixels per training class where N represents the number of bands.
Classification of visible and NIR resulted in a map (fig.4) that showed the eight land cover classes, while water body and vegetation classes were displayed without much misclassification, the other categories of classes such as urban/built-up and rock types were displayed with some amount of misclassification. This is perhaps due to the lower signature separability of urban and rocky classes in the VNIR bands. The overall accuracy and kappa coefficient values are 58.84 % and 0.54 respectively. Such a low accuracy is due to the mis-classification of the rock type and urban classes in the VNIR image data. The overall accuracy and kappa statistics for SWIR are 53% and 0.47 respectively. This is mainly due to larger inaccuracies contributed by water and vegetation classes and perhaps due to the lower spatial resolution (30m) (Fig. 4a-4b).
Since lower accuracy was obtained while classifying the VNIR and SWIR images, classification of fused images was attempted. The fused image was obtained by merging VNIR (15m resolution) and SWIR (30m resolution) bands to result in a 18m VNIR + SWIR images with 15m resolution. Classification was attempted on this image using the same training sites as that for VNIR image. The resulting classified image proved to be better than the classified image obtained from VNIR image. The four rock types were represented with greater accuracy in the same manner as water body and vegetation classes. Improved accuracy may be attributed to the complementary information provided by VNIR and SWIR images. While the information from SWIR image contributed to differentiate between rock types, information from the visible and NIR bands contributed to distinguish between waterbodies and vegetation classes. Thus an overall accuracy of 66% and kappa value of 0.61 was obtained .
In this study, classification was attempted not as a tool to evaluate the quality of the fusion. However, the training sites used for classification of VNIR image were also used for classification of the fused image. This was done to verify the accuracy of classification of fused images. It was found that accuracy of classification improves on fusion of images. The classification exercise was performed not to achieve very high levels of accuracy but to demonstrate that classification accuracy improves in fused images.
Conclusion
This study has evaluated the potential of ASTER VNIR and SWIR sensors for landuse / landcover mapping. The capabilities of individual sensors and the fused outputs were evaluated by visual observation and by image classification.
While the individual VNIR and SWIR images provide little information about land cover classes, it was observed that fusion of images obtained from these two sensors resulted in enhancement of land cover features for improved mapping of the same. Information in the VNIR image contributed to the enhancement of vegetation and water classes. Rock and soil units were enhanced due to the contribution by the information in the SWIR images. Improvement in spatial resolution of the SWIR images and the contribution of complementary information present in the SWIR images resulted in enhancement of soil and rock units. Improved contrast between waterbodies, vegetation and rockunits was also observed. Thus the potential of ASTER data and fusion of the VNIR and SWIR images for enhanced landcover mapping has been demonstrated in this paper. Further study is needed in the area of classification of fused ASTER images.
Acknowledgement
The authors thank the ERSDAC (Earth Remote Sensing Data Analysis Center), Japan for providing the ASTER images to them for ARO on ASTER data use. The rights on image data belong to the Ministry of Economy, Trade and Industry, Japan. Institute of Remote Sensing and Centre for Geoscience and Engineering, Anna University are thanked by KV for providing the necessary support for this research.
References
- Chavez, P. S. Sides, S. C. and Anderson, J. A. 1991. Comparison of three different methods to merge multi resolution and multi-spectral data: Landsat TM and SPOT panchromatic. Photogrammetric Engineering and Remote Sensing. Vol 57. No. 3. pp. 295-303.
- ERSDAC, 2000. Advanced Spaceborne Thermal Emission and Reflection Radiometer, URL: http://asterweb.jpl.nasa.gov/
- Goward.N., Huemmrich.F and Waring.H 1994, Visible-near infrared Spectral reflectence of landscape components in western oregon : Remote Sensing environment Vol.47, Pp190-203
- Lakshmi, K., 2000. Improved information extraction using image fusion techniques. Unpublished M.Tech Thesis. Anna University.
- Musa.M.K.A, Hussin.Y.A. 2000 Multi-data fusion for sustainable forest management : A case study from northern part of selangor, Malaysia, International archives of Photogrammetry and Remote Sensing Vol.XXXIII, part B7
- Pohl, C. 1999. Tools and methods for fusion of images of different spatial resolution. International Archives of Photogrammetry and Remote Sensing. Vol 32. Part 7-4-3. Valladolid. Spain.
- Pohl. C and J.L.Van Genderen,1998. Multisensor image fusion in remote sensing : Concepts, methods and applications. Int.J.Remote Sensing,19(5),pp.823-854
- Schowengerdt, R. T., 1997, Remote sensing models and methods for Image processing, 2nd edition, Academic Press, USA.
- Vani, K. 1999. Merging and Analysis of multi-resolution satellite images. Unpublished M.E Thesis, Anna University.