A Multi-kernel Joint Sparse Graph for SAR Image Segmentation
A Multi-kernel Joint Sparse Graph for SAR Image Segmentation
复制标题
SAR图像分割的多核联合稀疏图
DOI：
10.1109/jstars.2015.2502991
复制
发表时间：
2016
影响因子：
5.5
通讯作者：
Zhao Zhiqiang
中科院分区：
文献类型：
--
作者：
Gu Jing;Jiao Licheng;Yang Shuyuan;Liu Fang;Hou Biao;Zhao Zhiqiang
近年来，基于稀疏图的分类方法在模式识别和计算机视觉方面的研究越来越受到人们的关注。稀疏自表示方法具有良好的分类识别性能、噪声鲁棒性和数据自适应能力。本文提出了一种多核联合稀疏图（MKJS-graph）来分割合成孔径雷达（SAR）图像。首先，SAR图像被分割成许多超像素。然后，利用一种新的多核稀疏表示（MKSR）模型在高维投影空间中稀疏表达超像素的多个特征，以反映超像素的全局相似性；此外，通过建立mkjs图的邻域矩阵，将超像素的局部邻域空间相关性与全局相似度相结合，提高了分割性能。结合超像素的全局和局部结构，使mkjs图在分割受散斑噪声污染的SAR图像时具有良好的分类能力。通过一系列实验对模拟、ku波段和x波段SAR图像进行了测试，结果表明该方法在SAR图像分割方面比其他先进算法更具竞争力。
Recently, more and more attention has been drawn on the study of sparse graph-based classification with respect to pattern recognition and computer vision. Sparse self-representation method features good category distinguishing performance, noise robustness, and data-adaptiveness. In this paper, a multi-kernel joint sparse graph (MKJS-graph) is proposed to segment synthetic aperture radar (SAR) images. At first, an SAR image is over-segmented to many superpixels. Then, a new multikernel sparse representation (MKSR) model is used to express sparsely multiple features of the superpixels in high-dimensional projection space, which can reflect the global similarity of superpixels. Moreover, the local neighborhood spatial correlation of superpixels is combined with the global similarity of that to improve the segmentation performance by formulating the adjacent matrix for MKJS-graph. Integration of the global and local structures of the superpixels provides the MKJS-graph with favorable category distinguishing ability on segmenting SAR images polluted by speckle noise. The simulated, Ku-band, and X-band SAR images are tested through a series of experiments, and the results indicate that the proposed method is more competitive than other state-of-the-art algorithms in SAR image segmentation.