Genetic Algorithm + FCM for SAR Image Classification

In this application, we utilize Genetic Algorithm (GA) and Fuzzy C-Means (FCM) clustering algorithm to address SAR image classification problems. The primary objective is to segment images into either two or three distinct classes. The implementation leverages GA's global optimization capabilities to enhance classification accuracy through chromosome encoding of cluster centers and fitness function evaluation based on segmentation quality metrics. Simultaneously, FCM algorithm handles inherent image ambiguities by calculating membership degrees using Euclidean distance measures and iterative centroid updates. The integrated approach involves initializing population parameters, executing FCM clustering within GA's fitness evaluation, and applying crossover/mutation operations to optimize cluster centroids. This hybrid methodology achieves more accurate and reliable classification results by combining GA's evolutionary search with FCM's fuzzy partitioning, effectively addressing the complexities of SAR image classification challenges.