Generalized co-occurrence matrix for multispectral texture analysis

We present a new co-occurrence matrix based approach for multispectral texture analysis. The spectral and spatial domains of the multispectral textures are processed separately. The color space used in this study is represented by subspaces and it is classified by the averaged learning subspace method (ALSM). In the spatial domain we use a generalized co-occurrence matrix for vector valued pixels. The texture feature vectors are classified by the k-nearest neighbor (KNN) classifier and the multilayer perceptron (MLP) network. Experimental results of the multispectral texture segmentation are presented