Local Block Grouping with Napca Spatial Preprocessing for Hyperspectral Remote Sensing Imagery Sparse Unmixing

Abstract

Spatial regularization sparse unmixing (SRSU) has been widely studied and proved to be far better than the traditional spectral unmixing methods. These spatial sparse unmixing algorithms have obtained many competitive results except for the negative influences of inaccurate estimated unmixing abundances or outliers in abundances. In this paper, to obtain a more accurate SRSU results, a local block grouping with noise-adjusted principal component analysis method is used to do spatial preprocessing in sparse unmixing process. Here, local blocks are treated as a series of vector variables, and these variables are selected by grouping the pixels with similar local spatial structures to the underlying one in the local window. Then noise-adjusted principal component analysis (NAPCA) is taken to transform the original datasets into PCA domain and maintain only the most significant principal component as well as wipe off the inaccurate estimated fractional abundances. Compared with total variation-based and nonlocal means-based SRSU algorithms, the proposed joint local block grouping with NAPCA sparse unmixing method can yield competitive results with state-of-the-art spatial sparse unmixing algorithms using both simulated dataset and real hyperspectral imagery.