Fusion of ASTER image data for enhanced mapping of landcover features 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. |