论文标题
低级别的子空间表示，从最佳编码孔径进行高光谱图像的无监督分类
Low-Rank Subspace Representation from Optimal Coded-Aperture for Unsupervised Classification of Hyperspectral Imagery
论文作者
论文摘要
本文旨在通过直接从编码光圈快照光谱成像仪（CASSI）的压缩测量的光谱图像开发聚类方法。假设压缩度的测量通常大约位于与多个类相对应的低维子空间中，那么最先进的方法通常会分别为每个步骤获得最佳的解决方案，但不能保证它将达到全球最佳的聚类结果。在本文中，建议在压缩测量值上执行聚类，提出了低级别的子空间表示（LRSR）算法。此外，将子空间结构规范添加到低级别表示问题的目标中，该问题利用了一个事实，即子空间中的每个点可以表示为所有其他点的稀疏线性组合，并且每个子空间内的点的矩阵矩阵均为低等级。使用真实数据集的仿真说明了提出的光谱图像聚类方法的准确性。
This paper aims at developing a clustering approach with spectral images directly from the compressive measurements of coded aperture snapshot spectral imager (CASSI). Assuming that compressed measurements often lie approximately in low dimensional subspaces corresponding to multiple classes, state of the art methods generally obtains optimal solution for each step separately but cannot guarantee that it will achieve the globally optimal clustering results. In this paper, a low-rank subspace representation (LRSR) algorithm is proposed to perform clustering on the compressed measurements. In addition, a subspace structured norm is added into the objective of low-rank representation problem exploiting the fact that each point in a union of subspaces can be expressed as a sparse linear combination of all other points and that the matrix of the points within each subspace is low rank. Simulation with real dataset illustrates the accuracy of the proposed spectral image clustering approach.
