Possibilistic c-Means Algorithm Improving the Pixel Unmixing of Remotely Sensed Image
基于PCM改进算法的遥感混合像元模拟分析

The existence of mixed pixels is the main factor influencing the classification accuracy of remotely sensed image. Fuzzy classification is an important method of unmixing the mixed pixels. Its results depend on how accurate the membership value to various types of each pixel after classification corresponds to its actual component. If the clustering number is not equal to the actual type number in the unsupervised classification, or there are some types untrained in the supervised classification, the accuracy of the popular algorithm, namely Fuzzy c-means (FCM) will be degraded. Fortunately, Possibilistic c-means (PCM) is insensitive to it and can work well. This paper proposes the pixel unmixing method of remotely sensed image based on PCM algorithm. The priority of the PCM is illustrated by an actual example in the supervised classification in this paper.