Wavelet-Feature-Based Classifiers for Multispectral Remote-Sensing Images

The objective of this paper is to utilize the extracted features obtained by the wavelet transform (WT) rather than the original multispectral features of remote-sensing images for land-cover classification. WT provides the spatial and spectral characteristics of a pixel along with its neighbors, and hence, this can be utilized for an improved classification. Four classifiers, namely, the fuzzy product aggregation reasoning rule (FPARR), fuzzy explicit, multilayered perceptron, and neuro-fuzzy (NF), are used for this purpose. The performance is tested on multispectral real and synthetic images. The performance of original and wavelet-feature (WF)-based methods is compared. The WF-based methods have consistently yielded better results. Biorthogonal3.3 (Bior3.3) wavelet is found to be superior to other wavelets. FPARR along with the Bior3.3 wavelet outperformed all other methods. Results are evaluated using quantitative indexes like beta and Xie-Beni