A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing

Li, Chengbo; Sun, Tongfeng; Kelly, Kevin F.; Zhang, Yin

doi:10.1109/tip.2011.2167626

Cited by 150 publications

(18 citation statements)

References 35 publications

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“…Studies have shown that the huge HS datacubes are often highly redundant [13][14][15][16][17] and, therefore, very compressible or sparse. This gives the incentive to implement Compressive Sensing (CS) theory in HS systems.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal

Oiknine¹,

August²,

Farber³

et al. 2018

J. Imaging

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Hyperspectral (HS) imaging involves the sensing of a scene’s spectral properties, which are often redundant in nature. The redundancy of the information motivates our quest to implement Compressive Sensing (CS) theory for HS imaging. This article provides a review of the Compressive Sensing Miniature Ultra-Spectral Imaging (CS-MUSI) camera, its evolution, and its different applications. The CS-MUSI camera was designed within the CS framework and uses a liquid crystal (LC) phase retarder in order to modulate the spectral domain. The outstanding advantage of the CS-MUSI camera is that the entire HS image is captured from an order of magnitude fewer measurements of the sensor array, compared to conventional HS imaging methods.

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Section: Introductionmentioning

confidence: 99%

Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal

Oiknine¹,

August²,

Farber³

et al. 2018

J. Imaging

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show abstract

“…In the proposed method, we first use a coherent measurement matrix to compressively sense HSI and then use the VCA method to estimate the endmember directly from the HSI observations without recovering the images, which is a necessary step in the traditional method. As shown in the dashed parts in Figure 6 , if the proportion information

is required, one can use another incoherent measurement matrix to capture the global information of HSI, which can be used along with the estimated endmembers

to recover the proportions [ 5 , 20 ].…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…There are many methods for blind HU such as pixel purity index (PPI) [ 14 ], N-FINDR [ 15 ], vertex component analysis (VCA) [ 16 ], SSCBSS [ 17 ], hypGMCA [ 18 ], and modified VCA (MVCA) [ 19 ], which are all based on the Nyquist sampling theorem. There are also some HU methods based on the CS theory, such as CSU [ 20 ] and the method proposed in [ 5 ], but they all assume that the endmembers are known as side information. Endmember estimation is a key step to identify the materials in HSI, and in many applications, the endmembers are unknown.…”

Section: Introductionmentioning

confidence: 99%

Directly Estimating Endmembers for Compressive Hyperspectral Images

Qiao

et al. 2015

Sensors

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The large volume of hyperspectral images (HSI) generated creates huge challenges for transmission and storage, making data compression more and more important. Compressive Sensing (CS) is an effective data compression technology that shows that when a signal is sparse in some basis, only a small number of measurements are needed for exact signal recovery. Distributed CS (DCS) takes advantage of both intra- and inter- signal correlations to reduce the number of measurements needed for multichannel-signal recovery. HSI can be observed by the DCS framework to reduce the volume of data significantly. The traditional method for estimating endmembers (spectral information) first recovers the images from the compressive HSI and then estimates endmembers via the recovered images. The recovery step takes considerable time and introduces errors into the estimation step. In this paper, we propose a novel method, by designing a type of coherent measurement matrix, to estimate endmembers directly from the compressively observed HSI data via convex geometry (CG) approaches without recovering the images. Numerical simulations show that the proposed method outperforms the traditional method with better estimation speed and better (or comparable) accuracy in both noisy and noiseless cases.

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“…Hyperspectral imaging has increased significantly over the past decade, mainly because of a large amount of reference information, including the complete spectra of ground objects, which enables precise material identification using spectroscopic analysis [5]- [7]. There is a wide range of hyperspectral imaging applications such as terrain classification [8], remote surveillance [2], mineral detection and exploration, environmental monitoring, and military surveillance [9], and pharmaceutical process monitoring and quality control [10]. The main advantage of using hyperspectral imagery is that the spectral signature of each pixel can help identify the materials in the scene [11].…”

Section: Introductionmentioning

confidence: 99%

Fast Hyperspectral Unmixing via Reweighted Sparse Regression

Han

Guo

Wang

et al. 2019

IEICE Trans. Inf. & Syst.

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The sparse unmixing of hyperspectral data has attracted much attention in recent years because it does not need to estimate the number of endmembers nor consider the lack of pure pixels in a given hyperspectral scene. However, the high mutual coherence of spectral libraries strongly affects the practicality of sparse unmixing. The collaborative sparse unmixing via variable splitting and augmented Lagrangian (CLSUnSAL) algorithm is a classic sparse unmixing algorithm that performs better than other sparse unmixing methods. In this paper, we propose a CLSUnSAL-based hyperspectral unmixing method based on dictionary pruning and reweighted sparse regression. First, the algorithm identifies a subset of the original library elements using a dictionary pruning strategy. Second, we present a weighted sparse regression algorithm based on CLSUnSAL to further enhance the sparsity of endmember spectra in a given library. Third, we apply the weighted sparse regression algorithm on the pruned spectral library. The effectiveness of the proposed algorithm is demonstrated on both simulated and real hyperspectral datasets. For simulated data cubes (DC1, DC2 and DC3), the number of the pruned spectral library elements is reduced by at least 94% and the runtime of the proposed algorithm is less than 10% of that of CLSUnSAL. For simulated DC4 and DC5, the runtime of the proposed algorithm is less than 15% of that of CLSUnSAL. For the real hyperspectral datasets, the pruned spectral library successfully reduces the original dictionary size by 76% and the runtime of the proposed algorithm is 11.21% of that of CLSUnSAL. These experimental results show that our proposed algorithm not only substantially improves the accuracy of unmixing solutions but is also much faster than some other state-of-the-art sparse unmixing algorithms.

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A Compressive Sensing and Unmixing Scheme for Hyperspectral Data Processing

Cited by 150 publications

References 35 publications

Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal

Compressive Sensing Hyperspectral Imaging by Spectral Multiplexing with Liquid Crystal

Directly Estimating Endmembers for Compressive Hyperspectral Images

Fast Hyperspectral Unmixing via Reweighted Sparse Regression

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