Wavelet-fuzzy hybridization: Feature-extraction and land-cover classification of remote sensing images

A wavelet feature based supervised scheme for fuzzy classification of land covers in multispectral remote sensing images is proposed. The proposed scheme is developed in the framework of wavelet-fuzzy hybridization, a soft computing approach. The wavelet features obtained from wavelet transform on an image provides spatial and spectral characteristics (i.e., texture information) of pixels and hence can be utilized effectively for improving accuracy in classification, instead of using original spectral features. Four different fuzzy classifiers are considered for this purpose and evaluated using different wavelet features. Wavelet feature based fuzzy classifiers produced consistently better results compared to original spectral feature based methods on various images used in the present investigation. Further, the performance of the Biorthogonal3.3 (Bior3.3) wavelet is observed to be superior to other wavelets. This wavelet in combination with fuzzy product aggregation reasoning-rule outperformed all other methods. Potentiality of the proposed soft computing approach in isolating various land covers are evaluated both visually and quantitatively using indexes like β measure of homogeneity and Xie-Beni measure of compactness and separability.