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A technique is described for doing land cover classification using a neural network to integrate and classify SPOT multispectral and derived texture data. Orientated texture energy was derived from the higher spatial resolution SPOT panchromatic band with directional spatial filtering techniques. The multispectral and textural data were each clustered using a reported fuzzy learning vector quantization technique, which also produced fuzzy segmented images. The fuzzy membership values of data to clusters was then input to a multilayer neural network that had been trained using training samples of the land cover classes. Using an output layer that scales individual outputs to the range from zero to one and their sum to one, allows interpretation of the neural network outputs as class probabilities and the potential for post classification improvements by probabilistic relaxation techniques. Initial land cover classification results for neural network test and training data in a mixed rural-urban area are promising. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government

u.s. geological survey open file report/open-file report

Land cover classification from SPOT multispectral and panchromatic images using neural network classification of fuzzy clustered spectral and textural features