Geostatistical Texture Classification of Tropical Rainforest in Indonesia

Traditional spectral classification of remote sensing data applied on per pixel basis ignores the potentially useful spatial information between the values of proximate pixels. Although spatial information extraction has been greatly explored, there have been limited attempts to enhance classification by combining spectral and spatial information. This improvement would arise from the hypothesis that a pixel is not independent of its neighbors and, furthermore, that its dependence can be quantified and incorporated into the classifier. This study aims to explore the potential of utilizing texture spatial variability using geostatistics and Grey Level Co-occurrence Matrix (GLCM) texture measures. Different texture layers derived from geostatistics method, namely fractal dimension, semivariogram, madogram, rodogram, pseudo-cross variogram and pseudo-cross madogram, were incorporated for the land cover classification of tropical rainforests in East Kalimantan, Indonesia. Texture layers of grey level co-occurrence matrix (GLCM) channels, i.e. variance, contrast, dissimilarity, and homogeinity, were also used for the classification. Two classification methods, using Support Vector Machine and Minimum distance were applied for image classification. Landsat 7 ETM images combined with textural information is used for land cover classification of tropical rainforest area. Band 5 of Landsat data was used to compute texture layers using the GLCM and geostatistics methods. This band was chosen because it has the highest variance of training data compared to other spectral bands. The results were compared to find out how the extra information given by the texture enhances the classification. According to the accuracy assessment using error matrix, combinations of image and texture data performed better with 81% of accuracy compared to those of image data only with 76% of accuracy.