Joint Local Abundance Sparse Unmixing for Hyperspectral Images

Abstract

Sparse unmixing is widely used for hyperspectral imagery to estimate the optimal fraction (abundance) of materials contained in mixed pixels (endmembers) of a hyperspectral scene, by considering the abundance sparsity. This abundance has a unique property, i.e., high spatial correlation in local regions. This is due to the fact that the endmembers existing in the region are highly correlated. This implies the low-rankness of the abundance in terms of the endmember. From this prior knowledge, it is expected that considering the low-rank local abundance to the sparse unmixing problem improves estimation performance. In this study, we propose an algorithm that exploits the low-rank local abundance by applying the nuclear norm to the abundance matrix for local regions of spatial and abundance domains. In our optimization problem, the local abundance regularizer is collaborated with the L 2,1 norm and the total variation for sparsity and spatial information, respectively. We conducted experiments for real and simulated hyperspectral data sets assuming with and without the presence of pure pixels. The experiments showed that our algorithm yields competitive results and performs better than the conventional algorithms.

Hyperspectral Anomaly Detection Based on Separability-Aware Sample Cascade

“…Therefore, considering that such detectors adopting a sliding window strategy are usually sensitive to the window sizes, different pairs of window sizes are set in order to convincingly reflect their detection performance. Taking account of both characteristics of different hyperspectral images and requirements of different detectors' parameter settings, we define four pairs of window sizes for LRX- (17,7), (17,9), (19,7), and (19,9)-and six pairs of window sizes for SRD- (13,7), (15,9), (17,7), (17,9), (19,7), and (19,9). Besides, we set the optimal value for the regularized parameter of SRD for each hyperspectral image in the experiment.…”

“…These spectra are represented by hundreds of continuous bands that can meticulously describe the characteristics of different materials to recognize their subtle differences [3]. Therefore, owing to this good discriminative property of hyperspectral image, it has been widely used in many remote sensing research fields [4,5], such as image denoising [6,7], hyperspectral unmixing [8,9], band selection [10,11], target detection [12,13], and image classification [14,15]. They all have important practical applications in geological exploration, urban remote sensing and planning management, environment and disaster monitoring, precision agriculture, archaeology, etc.…”

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

“…In recent years, sparse unmixing acted as a semi-supervised spectral unmixing technique has attracted much attention [28][29][30][31], since it can better utilize the standard spectral library, which was collected and built under ideal condition, and circumvent the challenge of endmember selection. Then, the hyperspectral unmixing via sparse representation becomes a combination problem which amounts to determining the best combination of spectral signatures in the spectral library known in advance.…”

Fast Semi-Supervised Unmixing of Hyperspectral Image by Mutual Coherence Reduction and Recursive PCA

“…Popular sparse unmixing methods like-variable splitting and augmented Lagrangian (SUnSAL) [30] employed l 1 sparsity term, collaborative SUnSAL algorithm [31] combined collaborative sparse regression with the sparsity promoting term, whereas, SUnSAL-TV [32] introduced a total variation regularization term in the sparse unmixing. Among the sparse unmixing methods for abundance estimation robust sparse unmixing [33,34] method incorporates a redundant regularization term to account for endmember variability, joint local abundance method [35] performs local unmixing by exploiting structural information of image, co-evolutionary approach [36] formulates a multi-objective strategy and minimize it by evolutionary algorithm. Other works such as Feng et al [37] proposed a spatial regularization framework which employs maximum a posteriori estimation, Themelis et al [38] introduced a hierarchical Bayesian model based sparse unmixing method, Zhang et al [39] transform data in framelet domain and maximize the sparsity of the obtained abundance matrix, Zhu et al [40] proposed a correntropy maximization approach for sparse unmixing.…”