The Application of Run-Length Features in Remote Sensing Classification Combined with Neural Network and Rough Set

In this paper, we propose a method of remote sensing classification based on run-length features combined with neural network. According to the criterion of variances between & within classes, we choose efficient features and exclude redundant ones successfully with the method of rough set. In experiment, we use run-length features, co-occurrence features, gray-level gradient co-occurrence features and gray-level smoothed co-occurrence features respectively as inputs of three types of classifiers: BP net, RBF net and a nearest neighbor classifier: K-NN method when applying remote sensing classification for large scale panchromatic SPOT images with high spatial resolution. The result demonstrates the efficiency of the method proposed in this paper.