Remote Sensing Image Classification Using an Adaptive Min-Distance Algorithm
一种多光谱遥感图象的自适应最小距离分类方法

This article presents an adaptive min distance algorithm to classify multi spectral remote sensing images. This method approximates the distribution of the classes by dividing the sample sets, and adjusts the parameters of the min distance classifier adaptively. Experiments with TM remote sensing images demonstrate that this approach achieves an accuracy of 92.9% in the supervised classification of 16 classes. The experimental results verify the applicability of this approach in classifying of multicategory remote sensing images.