Fuzzy classification of Earth terrain covers using multi-look polarimetric SAR image data

The conventional approach of terrain image classification which assigns a specific class for each pixel is inadequate because the area covered by each pixel may embrace more than a single class. Fuzzy set theory which has been developed to deal with imprecise information can provide a more appropriate solution to this problem. In the paper, the authors used the fuzzy c-means clustering algorithm for the segmentation of a polarimetric SAR image. The distance measure utilized in the algorithm was derived from the complex Wishart distribution of the pixel data presented in the covariance matrix format. The algorithm computes the feature covariance matrix for each class and generates a fuzzy partition of the whole image. Classification of the image is achieved using a defuzzification criterion. The results are similar to those of supervised statistical methods. NASA/JPL AIRSAR data is used to substantiate this fuzzy classification algorithm