Spectral-Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images
Spectral-Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images
复制标题
高光谱遥感图像的谱空间稀疏子空间聚类
DOI：
10.1109/tgrs.2016.2524557
复制
发表时间：
2016-06-01
影响因子：
8.2
通讯作者：
Li, Pingxiang
中科院分区：
文献类型：
--
作者：
Zhang, Hongyan;Zhai, Han;Li, Pingxiang
由于高光谱图像本身的复杂性，对其进行聚类是一项非常具有挑战性的任务。提出了一种适用于高光谱遥感图像的光谱-空间稀疏子空间聚类(S4C)算法。首先，将各类土地覆盖类作为一个子空间，将稀疏子空间聚类(SSC)算法引入到HSIS中。然后，考虑到HIS的光谱和空间特性，在SSC模型中考虑了HIS的高光谱相关性和丰富的空间信息，得到了更精确的系数矩阵，并用于构建邻接矩阵。最后对邻接矩阵进行谱聚类，得到最终的聚类结果。通过多个实验验证了S4C算法的性能。
Clustering for hyperspectral images (HSIs) is a very challenging task due to its inherent complexity. In this paper, we propose a novel spectral-spatial sparse subspace clustering (S4C) algorithm for hyperspectral remote sensing images. First, by treating each kind of land-cover class as a subspace, we introduce the sparse subspace clustering (SSC) algorithm to HSIs. Then, considering the spectral and spatial properties of HSIs, the high spectral correlation and rich spatial information of the HSIs are taken into consideration in the SSC model to obtain a more accurate coefficient matrix, which is used to build the adjacent matrix. Finally, spectral clustering is applied to the adjacent matrix to obtain the final clustering result. Several experiments were conducted to illustrate the performance of the proposed S4C algorithm.