LAUSR

One important task of analyzing remote sensing satellite imagery is to categorize the pixels according to various landcover regions. This unsupervised segmentation task can be posed as a problem of pixel intensity clustering, considering the different spectral bands as different features. Due to the presence of noise and overlapping clusters present in remote sensing images, fuzzy clustering is popularly applied for the segmentation task. However, fuzzy C-means like fuzzy clustering algorithms suffer from random initialization that often causes them to get stuck into some local optimum results. In this article, a game theory-motivated approach based on Shapley value to initialize the cluster centers for FCM clustering has been adopted which provides more stability and improved performance. The proposed method explores and exploits the advantages of both game theory and fuzzy technique for pixel classification. The superiority of the approach has been demonstrated over a numeric satellite image data set as well as different real-life remote sensing satellite images both visually and numerically with statistical support. The results have been compared with several popular centroid-based clustering techniques, viz. K-means, K-means++, fuzzy C-means (FCM) and probabilistic FCM, using $$J_m$$ J m and I indexes as well as visual cluster plots.