Incremental Wishart Broad Learning System for Fast PolSAR Image Classification

In recent years, deep learning neural networks have seen wide adoption in synthetic aperture radar (SAR) image applications. Comparatively, convenient and fast neural network models have attracted less attention. In this letter, a novel incremental Wishart broad learning system (IWBLS) is specifically designed to achieve polarimetric SAR (PolSAR) image classification for the first time. IWBLS can effectively transfer essential Wishart distribution and other types of polarimetric decomposition and spatial features to establish mapped feature and enhancement nodes in one layer without deep learning structures, which means that massive layer-by-layer training consumption can be decreased significantly. Incremental learning concept is incorporated to deal with new PolSAR images or additional features, thereby avoiding retraining entire neural networks, whose properties are very appropriate for long-term monitoring or stepwise feature integration. The experiments substantiate advantages of PolSAR image classification based on our proposed IWBLS algorithm.