A segmentation based global iterative censoring scheme for ship detection in synthetic aperture radar image.doc

Abstract

This letter depicts a ship detection scheme for synthetic aperture radar images, utilizing a segmentation based global iterative censoring algorithm. In the proposed scheme, the fuzzy local information c-means clustering (RFLICM) algorithm is adopted to partition the inhomogeneous SAR image into numerous homogeneous sub-regions, thereby eliminating the performance degradation caused by SAR image inhomogeneity. Subsequently, successively applying the GIC algorithm base on a parametric clutter model database to the sub-regions, the optimal clutter models and the initial outlier map of the sub-regions are generated. A sliding window CFAR detector based on the selected clutter models and the initial outlier map is utilized to detect ships in the SAR image. In our experiment, we tested the proposed method on spaceborne SAR data, and its effectiveness was successfully demonstrated.