Spatial-Spectral Locality-Constrained Low-Rank Representation with Semi-Supervised Hypergraph Learning for Hyperspectral Image Classification.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(2017)
摘要
In this paper, we propose a novel hyperspectral image classification method based on spatial-spectral locality-constrained low-rank representation (LRR) and semi-supervised hypergraph learning. Specifically, we first represent the hyperspectral data via LRR due to its abilities in both recovering the low-rank structure of high-dimensional observations and dealing with the noises corrupted during i...
更多查看译文
关键词
Support vector machines,Adaptation models,Hyperspectral imaging,Imaging,Robustness,Principal component analysis
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络