This paper presents a novel two-stage image segmentation framework by artificial immune system (AIS) thereof to partition synthetic aperture radar (SAR) images. The following three crucial tough problems have not been completely solved in current SAR image processing community thus far: (1) the automatic ability of discovering the true number of categories in different types of land covers; (2) the skill of smoothing speckle noise in SAR imagery, which is different from classical Gaussian and Salt & Pepper noise; (3) the better clustering performance in segmenting thousands of highly contaminating pixels in SAR image. With above three problems as goals, an effective two-stage SAR image segmentation framework (TSIS) is discussed here. Firstly, a union filter, combing maximum likelihood estimator and partial nonlocal means filter, is designed. Afterwards, a searching algorithm with variable length of chromosomes is designed to automatically discover the clustering numbers in SAR images. Finally, an efficient multi-objective clustering paradigm in AIS and kernel mapping thereof to implement final image partition is proposed. To test its performance, a systematic comparison of TSIS versus three famous variations of fuzzy c-means (FCM) and two graph partitioning methods is given. Experiments results show that TSIS can provide an effective option in segmenting the SAR imagery. 
Two-stage SAR image segmentation framework with an efficient union filter and multi-objective kernel clustering