Superpixel-Driven Optimized Wishart Network for Fast PolSAR Image Classification Using Global ${k}$ -Means Algorithm

Limitation of optical remote sensing technology gave rise to synthetic aperture radar (SAR) imaging. SAR is a microwave imaging technique, which promises to have a long-range propagation characteristic allowing imaging under harsh weather conditions or in hostile lighting situation. This has opened up a domain of classification using polarimetric SAR (PolSAR) images. In this article, we propose a fast PolSAR image classification algorithm, which uses not only pixel-based feature but also spatial features around each pixel. This is achieved by introducing superpixel-driven optimized Wishart network. The first improvement suggested in this article is to take advantage of a fast global $k$ -means algorithm for obtaining optimal cluster centers within each class. It uses real-valued vector representation of PolSAR coherency matrix along with fast matrix inverse and determinant algorithms to reduce computational overhead. Our method then exploits the information of neighboring pixels by forming a superpixel so that even a noisy pixel may not be assigned a wrong class label. The proposed network uses dual-branch architecture to efficiently combine pixel and superpixel features. We concluded that our proposed method has better efficiency in terms of classification accuracy and computational overhead compared with other deep learning-based methods available in the literature.