Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

Abstract

Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression. In a spatial regularized sparse unmixing model, spatial consideration acts as an important role and develops from local neighborhood pixels to global structures. However, incorporating spatial relationships will increase the computational complexity, and it is inevitable that some negative influences obtained by inaccurate estimated abundances’ spatial correlations will reduce the accuracy of the algorithms. To obtain a more reliable and efficient spatial regularized sparse unmixing results, a joint local block grouping with noise-adjusted principal component analysis for hyperspectral remote-sensing imagery sparse unmixing is proposed in this paper. In this work, local block grouping is first utilized to gather and classify abundant spatial information in local blocks, and noise-adjusted principal component analysis is used to compress these series of classified local blocks and select the most significant ones. Then the representative spatial correlations are drawn and replace the traditional spatial regularization in the spatial regularized sparse unmixing method. Compared with total variation-based and non-local means-based sparse unmixing algorithms, the proposed approach can yield comparable experimental results with three simulated hyperspectral data cubes and two real hyperspectral remote-sensing images.

Moreau-Enhanced Total Variation and Subspace Factorization for Hyperspectral Denoising

“…To strongly induce sparsity, we introduce the new penalty function Ψ α (x) as the piecewise smoothness surrogate to substitute the TV norm in (5). Consequently, the Moreau-enhanced TV denoising model is formulated as mtvd(y; η, α) = arg min…”

“…With the wealth of spatial and spectral information, hyperspectral image (HSI) delivers a more accurate description ability of real scenes to distinguish precise details than color images and provides potential advantages of application in vegetation monitoring, medical diagnosis, mineral exploration, among numerous others [1,2]. However, due to the instrument restrictions and various weather conditions, HSIs are unavoidably corrupted by complicated forms of noise during the acquisition procedure [3], which significantly plagues the subsequent applications, such as classification [4], spectral unmixing [5,6], and anomaly detection [7,8]. As a result, denoising the HSIs become an essential preprocessing step to support the subsequent HSI-related applications and analysis.…”

Identification of Hydrothermal Alteration Minerals for Exploring Gold Deposits Based on SVM and PCA Using ASTER Data: A Case Study of Gulong

“…To improve the extraction accuracy of mineral alteration information, the SVM model, an excellent remote sensing image classification technology [48][49][50][51][52], was trained using alteration sample data by PCA. SVM firstly maps samples to high-dimensional spaces by nonlinear transformation; and then finds out the optimal classification hyperplane in high-dimensional space; and finally, classifies the sample data.…”

Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Endmember Independence and Spatial Weighted Abundance

“…The spatial discontinuity-weighted sparse unmixing [24] adopts a spatial discontinuity weight for SUnSAL to preserve the spatial details of abundance. The joint local block grouping with the noise-adjusted principal component analysis sparse method [25] utilizes the local block grouping to get spatial information and draws the representative spatial correlations obtained by the noise-adjusted principal component analysis for unmixing. Besides, with the intention to take advantage of the spatial information, spectral and spatial weighting factors are exploited by the spectral-spatial weighted sparse unmixing framework, imposing the sparsity of solution [26].…”