Comparative studies on feature extraction methods for multispectral remote sensing image classification

Feature extraction of multispectral remote sensing image is an important task before classifying the image. When land areas are clustered into groups of similar land cover, one of the most important things is to extract the key features of a given image. Usually multispectral remote sensing images have many bands, and there may have been much redundancy information and it becomes difficult to extract the key features of the image. Therefore, it is necessary to study methods regarding how to extract the main features of the image effectively. In this paper, five methods are comparatively studied to reduce the multi-bands into lower dimensions in order to extract the most available features. These methods include the Euclid distance measurement (EDM, the discrete measurement criteria function (DMCF), the minimum differentiated entropy (MDE), the probability distance criterion (PDC), and the principle component analysis (PCA) method. The advantage and disadvantage of each method are evaluated by the classification results.