Efficient sea–land segmentation using seeds learning and edge directed graph cut

Accurate sea–land segmentation using ratio of average constrained graph cut for polarimetric synthetic aperture radar data

“…Set ξ indicate the edges of all pairs of neighboring pixels. 3 Based on the image I and the edge set ξ, an undirected graph G ¼ hI; ξi can be built. In G, each node is connected to its neighboring pixels by so-called n-links and to the two terminal nodes by t-links.…”

“…[1][2][3][4][5] However, sea-land segmentation in SAR images is not as a simple work as in photographic images. In the latter one, thresholding methods, for instance the Otsu's method 6 and the local adaptive threshold method (LATM), 7 are frequently used and achieve satisfactory segmentation results.…”

“…[1][2][3]5,8,9 Among them, in the era of the single SAR images, the commonly used sea-land segmentation methods can only exploit the magnitude information and cannot achieve ideal results. As a comparison, recent algorithms using polarimetric SAR (PolSAR) data are populated thanks to the successful launch of several PolSAR satellites.…”

“…In this case, correct description of the boundary details is critical to the segmentation accuracy. 3 In this paper, we propose a graph cut (GC)-based approach integrating multipolarization features and edge information to address the sea-land segmentation problems for SAR images. GC is an efficient framework that can achieve satisfactory two-value segmentation by two steps: transferring an image to an undirected graph and finding a cut through the graph with minimal cumulative cost.…”

“…To further validate the effectiveness, we conduct comparative experiments with the canny edge directed GC that is proposed in Dongcai Cheng's paper. 3 In Cheng's work, the edge constraint in GC is the edge map that is generated by canny detector rather than ROA. The comparative results are shown in Fig.…”