Feature detection in satellite images using neural network technology

Abstract

This article describes a study of the feasibility of automated classification of satellite images. Satellite images were characterized by the textures they contain. In particular, the detection of cloud textures was investigated. The method of second-order gray level statistics, using co-occurrence matrices, was applied to extract feature vectors from image segments. Neural network technology was employed to classify these feature vectors. The Cascade-Correlation architecture was successfully used as a classifier. The use of a Kohonen netwokr was also investigated but this architecture could not reliably classify the feature vectors due to the complicated structure of the classification problem. The best results were obtained when data from different spectral bands were fused.