Low-rank representation for 3D hyperspectral images analysis from map perspective
A novel framework jointing the maximum a posteriori (MAP) model and low-rank representation (LRR) is proposed. The use of LRR can model feature selectivity and obtain a more compact and discriminative representation. The use of MAP model facilities us to exploit the connectivity of adjacent pixels in hyperspectral data.
maximum a posteriori (map) low-rank representation (lrr) 3d hyperspectral data semantic analysis segmentation markov random field (mrf) remote sensing