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A neuro-wavelet supervised classifier is proposed for land cover classification of multispectral remote sensing images. Features extracted from the original pixels using wavelet transform (WT) are fed as input to a feed forward multi-layer perceptron (MLP). A set of wavelets from different groups have been used and it is found that biorthogonal3.3 wavelet performs better. The performance is evaluated...
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...
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