樊娟;邓秀勤;火博丰;王卓薇;

• 1:广东工业大学数学与统计学院
• 2:青海师范大学数学与统计学院
• 3:藏语智能信息处理及应用国家重点实验室
• 4:广东工业大学计算机学院

摘要(Abstract)：

稀疏子空间在基于单稀疏表示系数构建相似矩阵的过程中，并没有充分利用高光谱图像丰富的光谱和空间信息。提出了一种基于弗雷歇距离的加权稀疏子空间聚类算法(FSSC)。该方法充分考虑了高光谱图像丰富的光谱信息以及光谱连续性，利用弗雷歇距离度量像素点光谱曲线间的相似度，并基于稀疏表示矩阵和相似度矩阵建立光谱加权稀疏子空间聚类模型，从而求解更真实的亲和力矩阵，以获得更高的地物分割精度。在两个经典的高光谱图像数据集上的实验结果表明了FSSC算法的有效性。

关键词(KeyWords)： 高光谱图像;稀疏子空间聚类;亲和力矩阵;弗雷歇距离

Abstract:

Keywords:

基金项目(Foundation): 广东省科技计划项目(2021A1414030004);; 广东省重点研发计划项目(2019B010109001);; 高分辨率对地观测重大专项省域产业化应用项目(83-Y40G33-9001-18/20);; 广东省信息物理融合系统重点实验室项目(2020B1212060069)

作者(Authors): 樊娟;邓秀勤;火博丰;王卓薇;

DOI: 10.13705/j.issn.1671-6841.2021318

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