Hyperspectral Image Reconstruction by Latent Low-Rank Representation for Classification

To effectively reduce the spectral variation that degrades classification performance, a novel low-rank subspace recovery method based on latent low-rank representation (LatLRR) is proposed for hyperspectral images in this letter. Different from the robust principal component analysis, LatLRR focuses on exploring the low-rank property from the perspective of row space and column space simultaneously through the low-rank regularization on their corresponding coefficient matrix. Following that, the self-expressiveness-based reconstruction is adopted to recover the intrinsic data from row and column spaces. More accurate subspace structure can be successfully preserved both in spectral domain and spatial domain; meanwhile, the robustness to noise is improved. Experimental results on two hyperspectral data sets demonstrate the effectiveness of the proposed method.