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Journal articles on the topic 'Sparse hyperspectral unmixing'

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Author: Grafiati

Published: 6 September 2023

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1

Deng, Chengzhi, Yaning Zhang, Shengqian Wang, Shaoquan Zhang, Wei Tian, Zhaoming Wu, and Saifeng Hu. "Approximate Sparse Regularized Hyperspectral Unmixing." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/947453.

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Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing imagery. And then, a variable splitting and augmented Lagrangian algorithm is introduced to tackle the optimization problem. In ASU, approximate sparsity is used as a regularizer for sparse unmixing, which is sparser thanl1regularizer and much easier to be solved thanl0regularizer. Three simulated and one real hyperspectral images were used to evaluate the performance of the proposed algorithm in comparison tol1regularizer. Experimental results demonstrate that the proposed algorithm is more effective and accurate for hyperspectral unmixing than state-of-the-artl1regularizer.

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2

Deng, Chengzhi, Yonggang Chen, Shaoquan Zhang, Fan Li, Pengfei Lai, Dingli Su, Min Hu, and Shengqian Wang. "Robust Dual Spatial Weighted Sparse Unmixing for Remotely Sensed Hyperspectral Imagery." Remote Sensing 15, no. 16 (August 16, 2023): 4056. http://dx.doi.org/10.3390/rs15164056.

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Sparse unmixing plays a crucial role in the field of hyperspectral image unmixing technology, leveraging the availability of pre-existing endmember spectral libraries. In recent years, there has been a growing trend in incorporating spatial information from hyperspectral images into sparse unmixing models. There is a strong spatial correlation between pixels in hyperspectral images (that is, the spatial information is very rich), and many sparse unmixing algorithms take advantage of this to improve the sparse unmixing effect. Since hyperspectral images are susceptible to noise, the feature separability of ground objects is reduced, which makes most sparse unmixing methods and models face the risk of degradation or even failure. To address this challenge, a novel robust dual spatial weighted sparse unmixing algorithm (RDSWSU) has been proposed for hyperspectral image unmixing. This algorithm effectively utilizes the spatial information present in the hyperspectral images to mitigate the impact of noise during the unmixing process. For the proposed RDSWSU algorithm, which is based on ℓ1 sparse unmixing framework, a pre-calculated superpixel spatial weighting factor is used to smooth the noise, so as to maintain the original spatial structure of hyperspectral images. The RDSWSU algorithm, which builds upon the ℓ1 sparse unmixing framework, employs a pre-calculated spatial weighting factor at the superpixel level. This factor aids in noise smoothing and helps preserve the inherent spatial structure of hyperspectral images throughout the unmixing process. Additionally, another spatial weighting factor is utilized in the RDSWSU algorithm to capture the local smoothness of abundance maps at the sub-region level. This factor helps enhance the representation of piecewise smooth variations within different regions of the hyperspectral image. Specifically, the combination of these two spatial weighting factors in the RDSWSU algorithm results in an enhanced sparsity of the abundance matrix. The RDSWSU algorithm, which is a sparse unmixing model, offers an effective solution using the alternating direction method of multiplier (ADMM) with reduced requirements for tuning the regularization parameter. The proposed RDSWSU method outperforms other advanced sparse unmixing algorithms in terms of unmixing performance, as demonstrated by the experimental results on synthetic and real hyperspectral datasets.

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Zhang, Shuaiyang, Wenshen Hua, Gang Li, Jie Liu, Fuyu Huang, and Qianghui Wang. "Double Regression-Based Sparse Unmixing for Hyperspectral Images." Journal of Sensors 2021 (September 3, 2021): 1–14. http://dx.doi.org/10.1155/2021/5575155.

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Sparse unmixing has attracted widespread attention from researchers, and many effective unmixing algorithms have been proposed in recent years. However, most algorithms improve the unmixing accuracy at the cost of large calculations. Higher unmixing accuracy often leads to higher computational complexity. To solve this problem, we propose a novel double regression-based sparse unmixing model (DRSUM), which can obtain better unmixing results with lower computational complexity. DRSUM decomposes the complex objective function into two simple formulas and completes the unmixing process through two sparse regressions. The unmixing result of the first sparse regression is added as a constraint to the second. DRSUM is an open model, and we can add different constraints to improve the unmixing accuracy. In addition, we can perform appropriate preprocessing to further improve the unmixing results. Under this model, a specific algorithm called double regression-based sparse unmixing via K -means ( DRSU M K − means ) is proposed. The improved K -means clustering algorithm is first used for preprocessing, and then we impose single sparsity and joint sparsity (using l 2 , 0 norm to control the sparsity) constraints on the first and second sparse unmixing, respectively. To meet the sparsity requirement, we introduce the row-hard-threshold function to solve the l 2 , 0 norm directly. Then, DRSU M K − means can be efficiently solved under alternating direction method of multipliers (ADMM) framework. Simulated and real data experiments have proven the effectiveness of DRSU M K − means .

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Feng, Ruyi, Lizhe Wang, and Yanfei Zhong. "Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing." Remote Sensing 11, no. 10 (May 23, 2019): 1223. http://dx.doi.org/10.3390/rs11101223.

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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.

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5

Li, Yalan, Yixuan Li, Wenwu Xie, Qian Du, Jing Yuan, Lin Li, and Chen Qi. "Adaptive multiscale sparse unmixing for hyperspectral remote sensing image." Computer Science and Information Systems, no. 00 (2023): 9. http://dx.doi.org/10.2298/csis220828009l.

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Sparse unmixing of hyperspectral images aims to separate the endmembers and estimate the abundances of mixed pixels. This approach is the essential step for many applications involving hyperspectral images. The multi scale spatial sparse hyperspectral unmixing algorithm (MUA) could achieve higher accuracy than many state-of-the-art algorithms. The regularization parameters, whose combinations markedly influence the unmixing accuracy, are determined by manually searching in the broad parameter space, leading to time consuming. To settle this issue, the adaptive multi-scale spatial sparse hyperspectral unmixing algorithm (AMUA) is proposed. Firstly, the MUA model is converted into a new version by using of a maximum a posteriori (MAP) system. Secondly, the theories indicating that andnorms are equivalent to Laplacian and multivariate Gaussian functions, respectively, are applied to explore the strong connections among the regularization parameters, estimated abundances and estimated noise variances. Finally, the connections are applied to update the regularization parameters adaptively in the optimization process of unmixing. Experimental results on both simulated data and real hyperspectral images show that the AMUA can substantially improve the unmixing efficiency at the cost of negligible accuracy. And a series of sensitive experiments were undertook to verify the robustness of the AMUA algorithm.

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6

Feng, Dan, Mingyang Zhang, and Shanfeng Wang. "Multipopulation Particle Swarm Optimization for Evolutionary Multitasking Sparse Unmixing." Electronics 10, no. 23 (December 5, 2021): 3034. http://dx.doi.org/10.3390/electronics10233034.

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Recently, the multiobjective evolutionary algorithms (MOEAs) have been designed to cope with the sparse unmixing problem. Due to the excellent performance of MOEAs in solving the NP hard optimization problems, they have also achieved good results for the sparse unmixing problems. However, most of these MOEA-based methods only deal with a single pixel for unmixing and are subjected to low efficiency and are time-consuming. In fact, sparse unmixing can naturally be seen as a multitasking problem when the hyperspectral imagery is clustered into several homogeneous regions, so that evolutionary multitasking can be employed to take advantage of the implicit parallelism from different regions. In this paper, a novel evolutionary multitasking multipopulation particle swarm optimization framework is proposed to solve the hyperspectral sparse unmixing problem. First, we resort to evolutionary multitasking optimization to cluster the hyperspectral image into multiple homogeneous regions, and directly process the entire spectral matrix in multiple regions to avoid dimensional disasters. In addition, we design a novel multipopulation particle swarm optimization method for major evolutionary exploration. Furthermore, an intra-task and inter-task transfer and a local exploration strategy are designed for balancing the exchange of useful information in the multitasking evolutionary process. Experimental results on two benchmark hyperspectral datasets demonstrate the effectiveness of the proposed method compared with the state-of-the-art sparse unmixing algorithms.

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7

Iordache, Marian-Daniel, José M. Bioucas-Dias, and Antonio Plaza. "Sparse Unmixing of Hyperspectral Data." IEEE Transactions on Geoscience and Remote Sensing 49, no. 6 (June 2011): 2014–39. http://dx.doi.org/10.1109/tgrs.2010.2098413.

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8

Sigurdsson, Jakob, Magnus O. Ulfarsson, Johannes R. Sveinsson, and Jose M. Bioucas-Dias. "Sparse Distributed Multitemporal Hyperspectral Unmixing." IEEE Transactions on Geoscience and Remote Sensing 55, no. 11 (November 2017): 6069–84. http://dx.doi.org/10.1109/tgrs.2017.2720539.

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9

Tong, Lei, Jing Yu, Chuangbai Xiao, and Bin Qian. "Hyperspectral unmixing via deep matrix factorization." International Journal of Wavelets, Multiresolution and Information Processing 15, no. 06 (November 2017): 1750058. http://dx.doi.org/10.1142/s0219691317500588.

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Hyperspectral unmixing is one of the most important techniques in hyperspectral remote sensing image analysis. During the past decades, many models have been widely used in hyperspectral unmixing, such as nonnegative matrix factorization (NMF) model, sparse regression model, etc. Most recently, a new matrix factorization model, deep matrix, is proposed and shows good performance in face recognition area. In this paper, we introduce the deep matrix factorization (DMF) for hyperspectral unmixing. In this method, the DMF method is applied for hyperspectral unmixing. Compared with the traditional NMF-based unmixing methods, DMF could extract more information with multiple-layer structures. An optimization algorithm is also proposed for DMF with two designed processes. Results on both synthetic and real data have validated the effectiveness of this method, and shown that it has outperformed several state-of-the-art unmixing approaches.

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10

Zhao, Genping, Fei Li, Xiuwei Zhang, Kati Laakso, and Jonathan Cheung-Wai Chan. "Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection." Remote Sensing 13, no. 20 (October 13, 2021): 4102. http://dx.doi.org/10.3390/rs13204102.

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Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal. To mitigate this problem, we present a method that applies spectral unmixing and structure sparse representation to accurately extract the pure background features and to establish a structured sparse representation model at a sub-pixel level by using the Archetypal Analysis (AA) scheme. Specifically, spectral unmixing with AA is used to unmix the spectral data to obtain representative background endmember signatures. Moreover the unmixing reconstruction error is utilized for the identification of the target. Structured sparse representation is also adopted for anomaly target detection by using the background endmember features from AA unmixing. Moreover, both the AA unmixing reconstruction error and the structured sparse representation reconstruction error are integrated together to enhance the anomaly target detection performance. The proposed method exploits background features at a sub-pixel level to improve the accuracy of anomaly target detection. Comparative experiments and analysis on public hyperspectral datasets show that the proposed algorithm potentially surpasses all the counterpart methods in anomaly target detection.

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11

Li, Fan. "Low-Rank and Spectral-Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery." Wireless Communications and Mobile Computing 2021 (October 31, 2021): 1–14. http://dx.doi.org/10.1155/2021/9374908.

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Sparse unmixing is an important technique for hyperspectral data analysis. Most sparse unmixing algorithms underutilize the spatial and spectral information of the hyperspectral image, which is unfavourable for the accuracy of endmember identification and abundance estimation. We propose a new spectral unmixing method based on the low-rank representation model and spatial-weighted collaborative sparsity, aiming to exploit structural information in both the spatial and spectral domains for unmixing. The spatial weights are incorporated into the collaborative sparse regularization term to enhance the spatial continuity of the image. Meanwhile, the global low-rank constraint is employed to maintain the spatial low-dimensional structure of the image. The model is solved by the well-known alternating direction method of multiplier, in which the abundance coefficients and the spatial weights are updated iteratively in the inner and outer loops, respectively. Experimental results obtained from simulation and real data reveal the superior performance of the proposed algorithm on spectral unmixing.

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12

Iordache, Marian-Daniel, Jose M. Bioucas-Dias, and Antonio Plaza. "Collaborative Sparse Regression for Hyperspectral Unmixing." IEEE Transactions on Geoscience and Remote Sensing 52, no. 1 (January 2014): 341–54. http://dx.doi.org/10.1109/tgrs.2013.2240001.

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13

Zhu, Feiyun, Ying Wang, Shiming Xiang, Bin Fan, and Chunhong Pan. "Structured Sparse Method for Hyperspectral Unmixing." ISPRS Journal of Photogrammetry and Remote Sensing 88 (February 2014): 101–18. http://dx.doi.org/10.1016/j.isprsjprs.2013.11.014.

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14

Zheng, Cheng Yong, Hong Li, Qiong Wang, and C. L. Philip Chen. "Reweighted Sparse Regression for Hyperspectral Unmixing." IEEE Transactions on Geoscience and Remote Sensing 54, no. 1 (January 2016): 479–88. http://dx.doi.org/10.1109/tgrs.2015.2459763.

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15

Wang, Dan, Zhenwei Shi, and Xinrui Cui. "Robust Sparse Unmixing for Hyperspectral Imagery." IEEE Transactions on Geoscience and Remote Sensing 56, no. 3 (March 2018): 1348–59. http://dx.doi.org/10.1109/tgrs.2017.2761912.

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16

Zheng, Yuhui, Feiyang Wu, Hiuk Jae Shim, and Le Sun. "Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior." Remote Sensing 11, no. 24 (December 4, 2019): 2897. http://dx.doi.org/10.3390/rs11242897.

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Hyperspectral unmixing is a key preprocessing technique for hyperspectral image analysis. To further improve the unmixing performance, in this paper, a nonlocal low-rank prior associated with spatial smoothness and spectral collaborative sparsity are integrated together for unmixing the hyperspectral data. The proposed method is based on a fact that hyperspectral images have self-similarity in nonlocal sense and smoothness in local sense. To explore the spatial self-similarity, nonlocal cubic patches are grouped together to compose a low-rank matrix. Then, based on the linear mixed model framework, the nuclear norm is constrained to the abundance matrix of these similar patches to enforce low-rank property. In addition, the local spatial information and spectral characteristic are also taken into account by introducing TV regularization and collaborative sparse terms, respectively. Finally, the results of the experiments on two simulated data sets and two real data sets show that the proposed algorithm produces better performance than other state-of-the-art algorithms.

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17

Feng, Ruyi, Lizhe Wang, and Yanfei Zhong. "Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery." Remote Sensing 10, no. 10 (September 25, 2018): 1546. http://dx.doi.org/10.3390/rs10101546.

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Sparse unmixing has been successfully applied in hyperspectral remote sensing imagery analysis based on a standard spectral library known in advance. This approach involves reformulating the traditional linear spectral unmixing problem by finding the optimal subset of signatures in this spectral library using the sparse regression technique, and has greatly improved the estimation of fractional abundances in ubiquitous mixed pixels. Since the potentially large standard spectral library can be given a priori, the most challenging task is to compute the regression coefficients, i.e., the fractional abundances, for the linear regression problem. There are many mathematical techniques that can be used to deal with the spectral unmixing problem; e.g., ordinary least squares (OLS), constrained least squares (CLS), orthogonal matching pursuit (OMP), and basis pursuit (BP). However, due to poor prediction accuracy and non-interpretability, the traditional methods often cannot obtain satisfactory estimations or achieve a reasonable interpretation. In this paper, to improve the regression accuracy of sparse unmixing, least angle regression-based constrained sparse unmixing (LARCSU) is introduced to further enhance the precision of sparse unmixing. Differing from the classical greedy algorithms and some of the cautious sparse regression-based approaches, the LARCSU algorithm has two main advantages. Firstly, it introduces an equiangular vector to seek the optimal regression steps based on the simple underlying geometry. Secondly, unlike the alternating direction method of multipliers (ADMM)-based algorithms that introduce one or more multipliers or augmented terms during their optimization procedures, no parameters are required in the computational process of the LARCSU approach. The experimental results obtained with both simulated datasets and real hyperspectral images confirm the effectiveness of LARCSU compared with the current state-of-the-art spectral unmixing algorithms. LARCSU can obtain a better fractional abundance map, as well as a higher unmixing accuracy, with the same order of magnitude of computational effort as the CLS-based methods.

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Zou, Jinlin, and Jinhui Lan. "A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing." Remote Sensing 11, no. 5 (March 1, 2019): 500. http://dx.doi.org/10.3390/rs11050500.

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Due to the complex background and low spatial resolution of the hyperspectral sensor, observed ground reflectance is often mixed at the pixel level. Hyperspectral unmixing (HU) is a hot-issue in the remote sensing area because it can decompose the observed mixed pixel reflectance. Traditional sparse hyperspectral unmixing often leads to an ill-posed inverse problem, which can be circumvented by spatial regularization approaches. However, their adoption has come at the expense of a massive increase in computational cost. In this paper, a novel multiscale hierarchical model for a method of sparse hyperspectral unmixing is proposed. The paper decomposes HU into two domain problems, one is in an approximation scale representation based on resampling the method’s domain, and the other is in the original domain. The use of multiscale spatial resampling methods for HU leads to an effective strategy that deals with spectral variability and computational cost. Furthermore, the hierarchical strategy with abundant sparsity representation in each layer aims to obtain the global optimal solution. Both simulations and real hyperspectral data experiments show that the proposed method outperforms previous methods in endmember extraction and abundance fraction estimation, and promotes piecewise homogeneity in the estimated abundance without compromising sharp discontinuities among neighboring pixels. Additionally, compared with total variation regularization, the proposed method reduces the computational time effectively.

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19

Wang, Zhao, Jinxin Wei, Jianzhao Li, Peng Li, and Fei Xie. "Evolutionary Multiobjective Optimization with Endmember Priori Strategy for Large-Scale Hyperspectral Sparse Unmixing." Electronics 10, no. 17 (August 27, 2021): 2079. http://dx.doi.org/10.3390/electronics10172079.

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Mixed pixels inevitably appear in the hyperspectral image due to the low resolution of the sensor and the mixing of ground objects. Sparse unmixing, as an emerging method to solve the problem of mixed pixels, has received extensive attention in recent years due to its robustness and high efficiency. In theory, sparse unmixing is essentially a multiobjective optimization problem. The sparse endmember term and the reconstruction error term can be regarded as two objectives to optimize simultaneously, and a series of nondominated solutions can be obtained as the final solution. However, the large-scale spectral library poses a challenge due to the high-dimensional number of spectra, it is difficult to accurately extract a few active endmembers and estimate their corresponding abundance from hundreds of spectral features. In order to solve this problem, we propose an evolutionary multiobjective hyperspectral sparse unmixing algorithm with endmember priori strategy (EMSU-EP) to solve the large-scale sparse unmixing problem. The single endmember in the spectral library is used to reconstruct the hyperspectral image, respectively, and the corresponding score of each endmember can be obtained. Then the endmember scores are used as a prior knowledge to guide the generation of the initial population and the new offspring. Finally, a series of nondominated solutions are obtained by the nondominated sorting and the crowding distances calculation. Experiments on two benchmark large-scale simulated data to demonstrate the effectiveness of the proposed algorithm.

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20

Wei, Jiaojiao, and Xiaofei Wang. "An Overview on Linear Unmixing of Hyperspectral Data." Mathematical Problems in Engineering 2020 (August 25, 2020): 1–12. http://dx.doi.org/10.1155/2020/3735403.

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Hyperspectral remote sensing technology has a strong capability for ground object detection due to the low spatial resolution of hyperspectral imaging spectrometers. A single pixel that leads to a hyperspectral remote sensing image usually contains more than one feature coverage type, resulting in a mixed pixel. The existence of a mixed pixel affects the accuracy of the ground object identification and classification and hinders the application and development of hyperspectral technology. For the problem of unmixing of mixed pixels in hyperspectral images (HSIs), the linear mixing model can model the mixed pixels well. Through the collation of nearly five years of the literature, this paper introduces the development status and problems of linear unmixing models from four aspects: geometric method, nonnegative matrix factorization (NMF), Bayesian method, and sparse unmixing.

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21

Lin, H., X. Zhang, X. Wu, J. D. Tarnas, and J. F. Mustard. "TARGET TRANSFORMATION CONSTRAINED SPARSE UNMIXING (TTCSU) ALGORITHM FOR RETRIEVING HYDROUS MINERALS ON MARS: APPLICATION TO SOUTHWEST MELAS CHASMA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 1003–8. http://dx.doi.org/10.5194/isprs-archives-xlii-3-1003-2018.

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Quantitative analysis of hydrated minerals from hyperspectral remote sensing data is fundamental for understanding Martian geologic process. Because of the difficulties for selecting endmembers from hyperspectral images, a sparse unmixing algorithm has been proposed to be applied to CRISM data on Mars. However, it's challenge when the endmember library increases dramatically. Here, we proposed a new methodology termed Target Transformation Constrained Sparse Unmixing (TTCSU) to accurately detect hydrous minerals on Mars. A new version of target transformation technique proposed in our recent work was used to obtain the potential detections from CRISM data. Sparse unmixing constrained with these detections as prior information was applied to CRISM single-scattering albedo images, which were calculated using a Hapke radiative transfer model. This methodology increases success rate of the automatic endmember selection of sparse unmixing and could get more accurate abundances. CRISM images with well analyzed in Southwest Melas Chasma was used to validate our methodology in this study. The sulfates jarosite was detected from Southwest Melas Chasma, the distribution is consistent with previous work and the abundance is comparable. More validations will be done in our future work.

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HAN, Hongwei, Ke GUO, Maozhi WANG, Tingbin ZHANG, and Shuang ZHANG. "Fast Hyperspectral Unmixing via Reweighted Sparse Regression." IEICE Transactions on Information and Systems E102.D, no. 9 (September 1, 2019): 1819–32. http://dx.doi.org/10.1587/transinf.2018edp7374.

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Li, Fan, Shaoquan Zhang, Chengzhi Deng, Bingkun Liang, Jingjing Cao, and Shengqian Wang. "Robust Double Spatial Regularization Sparse Hyperspectral Unmixing." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 12569–82. http://dx.doi.org/10.1109/jstars.2021.3132164.

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Li, Chang, Yong Ma, Xiaoguang Mei, Chengyin Liu, and Jiayi Ma. "Hyperspectral Unmixing with Robust Collaborative Sparse Regression." Remote Sensing 8, no. 7 (July 11, 2016): 588. http://dx.doi.org/10.3390/rs8070588.

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Lu, Xiaoqiang, Hao Wu, Yuan Yuan, Pingkun Yan, and Xuelong Li. "Manifold Regularized Sparse NMF for Hyperspectral Unmixing." IEEE Transactions on Geoscience and Remote Sensing 51, no. 5 (May 2013): 2815–26. http://dx.doi.org/10.1109/tgrs.2012.2213825.

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26

Zhao, Xi-Le, Fan Wang, Ting-Zhu Huang, Michael K. Ng, and Robert J. Plemmons. "Deblurring and Sparse Unmixing for Hyperspectral Images." IEEE Transactions on Geoscience and Remote Sensing 51, no. 7 (July 2013): 4045–58. http://dx.doi.org/10.1109/tgrs.2012.2227764.

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Zhang, Guixu, Yingying Xu, and Faming Fang. "Framelet-Based Sparse Unmixing of Hyperspectral Images." IEEE Transactions on Image Processing 25, no. 4 (April 2016): 1516–29. http://dx.doi.org/10.1109/tip.2016.2523345.

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28

Wang, Wenhong, Yuntao Qian, and Yuan Yan Tang. "Hypergraph-Regularized Sparse NMF for Hyperspectral Unmixing." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 2 (February 2016): 681–94. http://dx.doi.org/10.1109/jstars.2015.2508448.

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Wang, Rui, Heng-Chao Li, Wenzhi Liao, Xin Huang, and Wilfried Philips. "Centralized Collaborative Sparse Unmixing for Hyperspectral Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 5 (May 2017): 1949–62. http://dx.doi.org/10.1109/jstars.2017.2651063.

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30

Liu, Yang, Yi Guo, Feng Li, Lei Xin, and Puming Huang. "Sparse Dictionary Learning for Blind Hyperspectral Unmixing." IEEE Geoscience and Remote Sensing Letters 16, no. 4 (April 2019): 578–82. http://dx.doi.org/10.1109/lgrs.2018.2878036.

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Uezato, Tatsumi, Mathieu Fauvel, and Nicolas Dobigeon. "Hierarchical Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing with Spectral Variability." Remote Sensing 12, no. 14 (July 20, 2020): 2326. http://dx.doi.org/10.3390/rs12142326.

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Accounting for endmember variability is a challenging issue when unmixing hyperspectral data. This paper models the variability that is associated with each endmember as a conical hull defined by extremal pixels from the data set. These extremal pixels are considered as so-called prototypal endmember spectra that have meaningful physical interpretation. Capitalizing on this data-driven modeling, the pixels of the hyperspectral image are then described as combinations of these prototypal endmember spectra weighted by bundling coefficients and spatial abundances. The proposed unmixing model not only extracts and clusters the prototypal endmember spectra, but also estimates the abundances of each endmember. The performance of the approach is illustrated thanks to experiments conducted on simulated and real hyperspectral data and it outperforms state-of-the-art methods.

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Kong, Fanqiang, Wenjun Guo, Yunsong Li, Qiu Shen, and Xin Liu. "Backtracking-Based Simultaneous Orthogonal Matching Pursuit for Sparse Unmixing of Hyperspectral Data." Mathematical Problems in Engineering 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/842017.

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Sparse unmixing is a promising approach in a semisupervised fashion by assuming that the observed signatures of a hyperspectral image can be expressed in the form of linear combination of only a few spectral signatures (endmembers) in an available spectral library. Simultaneous orthogonal matching pursuit (SOMP) algorithm is a typical simultaneous greedy algorithm for sparse unmixing, which involves finding the optimal subset of signatures for the observed data from a spectral library. But the numbers of endmembers selected by SOMP are still larger than the actual number, and the nonexisting endmembers will have a negative effect on the estimation of the abundances corresponding to the actual endmembers. This paper presents a variant of SOMP, termed backtracking-based SOMP (BSOMP), for sparse unmixing of hyperspectral data. As an extension of SOMP, BSOMP incorporates a backtracking technique to detect the previous chosen endmembers’ reliability and then deletes the unreliable endmembers. Through this modification, BSOMP can select the true endmembers more accurately than SOMP. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed algorithm.

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Dong, Le, and Yuan Yuan. "Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing." Remote Sensing 13, no. 8 (April 11, 2021): 1473. http://dx.doi.org/10.3390/rs13081473.

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Recently, non-negative tensor factorization (NTF) as a very powerful tool has attracted the attention of researchers. It is used in the unmixing of hyperspectral images (HSI) due to its excellent expression ability without any information loss when describing data. However, most of the existing unmixing methods based on NTF fail to fully explore the unique properties of data, for example, low rank, that exists in both the spectral and spatial domains. To explore this low-rank structure, in this paper we learn the different low-rank representations of HSI in the spectral, spatial and non-local similarity modes. Firstly, HSI is divided into many patches, and these patches are clustered multiple groups according to the similarity. Each similarity group can constitute a 4-D tensor, including two spatial modes, a spectral mode and a non-local similarity mode, which has strong low-rank properties. Secondly, a low-rank regularization with logarithmic function is designed and embedded in the NTF framework, which simulates the spatial, spectral and non-local similarity modes of these 4-D tensors. In addition, the sparsity of the abundance tensor is also integrated into the unmixing framework to improve the unmixing performance through the L2,1 norm. Experiments on three real data sets illustrate the stability and effectiveness of our algorithm compared with five state-of-the-art methods.

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Wang, Peng, Xun Shen, Kang Ni, and Lixin Shi. "Hyperspectral sparse unmixing based on multiple dictionary pruning." International Journal of Remote Sensing 43, no. 7 (April 3, 2022): 2712–34. http://dx.doi.org/10.1080/01431161.2022.2068358.

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Xu Chenguang, 徐晨光, 邓承志 Deng Chengzhi, and 朱华生 Zhu Huasheng. "Approximate sparse regularized multilayer NMF for hyperspectral unmixing." Infrared and Laser Engineering 47, no. 11 (2018): 1117010. http://dx.doi.org/10.3788/irla201847.1117010.

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Esmaeili Salehani, Yaser, Saeed Gazor, Il-Min Kim, and Shahram Yousefi. "ℓ0-Norm Sparse Hyperspectral Unmixing Using Arctan Smoothing." Remote Sensing 8, no. 3 (February 26, 2016): 187. http://dx.doi.org/10.3390/rs8030187.

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Rizkinia, Mia, and Masahiro Okuda. "Joint Local Abundance Sparse Unmixing for Hyperspectral Images." Remote Sensing 9, no. 12 (November 27, 2017): 1224. http://dx.doi.org/10.3390/rs9121224.

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Iordache, Marian-Daniel, José M. Bioucas-Dias, and Antonio Plaza. "Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing." IEEE Transactions on Geoscience and Remote Sensing 50, no. 11 (November 2012): 4484–502. http://dx.doi.org/10.1109/tgrs.2012.2191590.

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Zhang, Shaoquan, Jun Li, Kai Liu, Chengzhi Deng, Lin Liu, and Antonio Plaza. "Hyperspectral Unmixing Based on Local Collaborative Sparse Regression." IEEE Geoscience and Remote Sensing Letters 13, no. 5 (May 2016): 631–35. http://dx.doi.org/10.1109/lgrs.2016.2527782.

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Salehani, Yaser Esmaeili, and Saeed Gazor. "Smooth and Sparse Regularization for NMF Hyperspectral Unmixing." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 8 (August 2017): 3677–92. http://dx.doi.org/10.1109/jstars.2017.2684132.

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Zhang, Zuoyu, Shouyi Liao, Hao Fang, Hexin Zhang, and Shicheng Wang. "Sparse Hyperspectral Unmixing Using Spectral Library Adaptive Adjustment." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 12 (December 2019): 4873–87. http://dx.doi.org/10.1109/jstars.2019.2939829.

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Su, Yuanchao, Andrea Marinoni, Jun Li, Javier Plaza, and Paolo Gamba. "Stacked Nonnegative Sparse Autoencoders for Robust Hyperspectral Unmixing." IEEE Geoscience and Remote Sensing Letters 15, no. 9 (September 2018): 1427–31. http://dx.doi.org/10.1109/lgrs.2018.2841400.

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Chen, F., K. Wang, and T. F. Tang. "Hyperspectral image unmixing using a sparse Bayesian model." Remote Sensing Letters 5, no. 7 (July 3, 2014): 642–51. http://dx.doi.org/10.1080/2150704x.2014.951096.

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Tang, Wei, Zhenwei Shi, and Zhana Duren. "Sparse hyperspectral unmixing using an approximate L0 norm." Optik 125, no. 1 (January 2014): 31–38. http://dx.doi.org/10.1016/j.ijleo.2013.06.073.

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Lu, Xiaoqiang, Le Dong, and Yuan Yuan. "Subspace Clustering Constrained Sparse NMF for Hyperspectral Unmixing." IEEE Transactions on Geoscience and Remote Sensing 58, no. 5 (May 2020): 3007–19. http://dx.doi.org/10.1109/tgrs.2019.2946751.

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Yuan Yuan, Min Fu, and Xiaoqiang Lu. "Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing." IEEE Transactions on Geoscience and Remote Sensing 53, no. 6 (June 2015): 2975–86. http://dx.doi.org/10.1109/tgrs.2014.2365953.

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Ma, Yong, Chang Li, Xiaoguang Mei, Chengyin Liu, and Jiayi Ma. "Robust Sparse Hyperspectral Unmixing With $\ell_{2,1}$ Norm." IEEE Transactions on Geoscience and Remote Sensing 55, no. 3 (March 2017): 1227–39. http://dx.doi.org/10.1109/tgrs.2016.2616161.

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Shi, Zhenwei, Tianyang Shi, Min Zhou, and Xia Xu. "Collaborative Sparse Hyperspectral Unmixing Using $l_{0}$ Norm." IEEE Transactions on Geoscience and Remote Sensing 56, no. 9 (September 2018): 5495–508. http://dx.doi.org/10.1109/tgrs.2018.2818703.

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Zhang, Shaoquan, Jun Li, Zebin Wu, and Antonio Plaza. "Spatial Discontinuity-Weighted Sparse Unmixing of Hyperspectral Images." IEEE Transactions on Geoscience and Remote Sensing 56, no. 10 (October 2018): 5767–79. http://dx.doi.org/10.1109/tgrs.2018.2825457.

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Li, Yan, Shaoquan Zhang, Chengzhi Deng, and Shengqian Wang. "Reweighted local collaborative sparse regression for hyperspectral unmixing." Infrared Physics & Technology 97 (March 2019): 277–86. http://dx.doi.org/10.1016/j.infrared.2018.12.030.

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