Exploring error of linear mixed model for hyperspectral image reconstruction from spectral compressive sensing

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

et al. 2020

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Hyperspectral compressed sensing (HCS) based on spectral unmixing technique has shown great reconstruction performance. In particular, the linear mixed model (LMM) has been widely used in HCS reconstruction. However, due to the complexity of environmental conditions, instrumental configurations, and material nonlinear mixing effects, LMM cannot accurately represent the hyperspectral images, which limits the improvement of reconstruction quality. In this article, first, by introducing spectral variability, nonlinear mixing, and residuals, a multitype mixed model (MMM) is proposed to establish a more accurate hyperspectral image model. Then, a novel MMM-based HCS is proposed, which performs spectral compressed sampling at the sampling stage only, and at the reconstruction stage, by using spectral unmixing, an MMM-based HCS super-resolution reconstruction algorithm from spectral compressed sensing data is developed, and the alternating direction multiplier method is employed to estimate each component of the MMM, furthermore, reasonable prior knowledge of each component is introduced to improve the estimation accuracy. Experimental results on hyperspectral datasets demonstrate that the proposed model outperforms those state-of-the-art methods based on the LMM in terms of HCS reconstruction quality. Index Terms-Compressed sensing, hyperspectral remote sensing, linear mixing model (LMM), spectral unmixing. I. INTRODUCTION H YPERSPECTRAL images (HSIs) can provide detailed ground features and are widely used in mineral exploration, agricultural production, environmental monitoring, and Manuscript

Section: A Spectral Compressed Sampling Of Hsismentioning

confidence: 99%

mentioning

confidence: 99%

Reconstruction of Hyperspectral Images From Spectral Compressed Sensing Based on a Multitype Mixing Model

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

et al. 2020

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“…The application of CS in the hyperspectral imaging field has also received wide attention. A series of hyperspectral compressive sensing (HCS) schemes have been proposed [8][9][10][11][12][13][14][15][16][17][18][19][20]. The special three-dimensional (3D) data structure allows diverse CS methods for HSIs.…”

Section: Introductionmentioning

confidence: 99%

“…The special three-dimensional (3D) data structure allows diverse CS methods for HSIs. Note that HSIs can be either spatially sampled, similar to ordinary images, or spectrally sampled [9,20]. However, spatial and spectral joint is a common method for HSIs processing [21].…”

Section: Introductionmentioning

confidence: 99%

Spatial-Spectral Joint Compressed Sensing for Hyperspectral Images

Hua

et al. 2020

IEEE Access

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Compressed sensing is one of the key technologies to reduce the volume of hyperspectral image for real-time storage and transmission. Reconstruction based on spectral unmixing show tremendous potential in hyperspectral compressed sensing compared with other conventional algorithms that directly reconstruct images. In this paper, a joint spatial-spectral joint compressed sensing scheme is proposed. In this scheme, compressed hyperspectral data are collected by spatial-spectral hybrid compressed sampling. As for the reconstruction, an objective function is developed by introducing the fidelity constraints of spatial and spectral measurements and the row sparsity constraint of abundance that guarantee the precise reconstruction of hyperspectral images and obtain endmembers and abundances as by-products accurately. An augmented Lagrangian type algorithm is meticulously elaborated to solve the above optimization problem. Extensive experimental results on several real hyperspectral datasets indicate that the proposed approach can achieve a reconstruction accuracy higher than those of other state-of-the-art methods. The efficiency and feasibility of the proposed scheme give it great potential in hyperspectral compressed sensing.

Semi-NMF-Based Reconstruction for Hyperspectral Compressed Sensing

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

et al. 2020

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Hyperspectral compressed sensing (HCS) is a new imaging method that effectively reduces the power consumption of data acquisition. In this paper, we present a novel HCS algorithm by incorporating spatial-spectral hybrid compressed sensing, followed by a reconstruction based on spectral unmixing. At the sampling stage, the measurements are acquired by a spatial-spectral hybrid compressive sampling scheme to preserve the necessary information for the following spectral unmixing, where spatial compressive sampling mainly retains the endmember information, and spectral compressive sampling mainly retains the abundance information. Due to the limitations of the traditional linear mixed model (LMM), an improved mixed model is proposed for HCS reconstruction, which considers spectral variability, nonlinear mixing and other factors. At the reconstruction stage, based on the improved mixed model, semi-nonnegative matrix factorization (semi-NMF) is introduced to realize spectral unmixing on the measurements to achieve the final reconstruction by using an alternate iteration manner. The proposed algorithm is tested on real hyperspectral data and the selection of parameters is fully analyzed. Experimental results demonstrate that the proposed algorithm can significantly outperform state-of-the-art HCS algorithms in terms of reconstruction performance.