Sea-Land Segmentation in Maritime Surveillance Radars via K-Nearest Neighbor Classifier

Shipborne or airborne maritime surveillance radars at scan mode work at a complex scene consisting of land, sea, and islands. Sea-land segmentation provides a two-class classification of sea clutter versus ground clutter to the surveillance scene as a precondition of adaptive target detection. It is a difficult problem because only a few coherent pulses are available at scan mode. Moreover, due to moving radar platforms and wide dynamic range of ground and sea clutter in power, average amplitude, Doppler offset, and initial phase of radar, the returns vector fails to distinguish sea clutter and ground clutter. In this article, a similarity measure of two radar returns vectors, which is invariant to amplitude, Doppler offset, and initial phase, is constructed, which is closely relevant to the Doppler bandwidth of a returns vector. Based on the similarity measure, a K-nearest neighbor classifier is proposed to yield a pixel-level sea-land segmentation of the scene. Further, the morphological filtering is operated on the pixel-level segmentation to obtain a region-level segmentation. Moreover, the discrete Frechet distances of the main boundaries in successive scan periods are used to assess segmentation quality. The proposed method is verified by measured data from an airborne radar and an island-based radar. The results show that it behaves better than the methods using thresholding phase linearity degree of radar returns and the support vector machine, and back propagation neural network in a three-dimensional feature space.