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Relevant bibliographies by topics / Sparse hyperspectral unmixing

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Academic literature on the topic 'Sparse hyperspectral unmixing'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Sparse hyperspectral unmixing"

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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Sparse hyperspectral unmixing"

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Vila, Jeremy P. "Empirical-Bayes Approaches to Recovery of Structured Sparse Signals via Approximate Message Passing." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429191048.

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Wei, Qi. "Bayesian fusion of multi-band images : A powerful tool for super-resolution." Phd thesis, Toulouse, INPT, 2015. http://oatao.univ-toulouse.fr/14398/1/wei.pdf.

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Hyperspectral (HS) imaging, which consists of acquiring a same scene in several hundreds of contiguous spectral bands (a three dimensional data cube), has opened a new range of relevant applications, such as target detection [MS02], classification [C.-03] and spectral unmixing [BDPD+12]. However, while HS sensors provide abundant spectral information, their spatial resolution is generally more limited. Thus, fusing the HS image with other highly resolved images of the same scene, such as multispectral (MS) or panchromatic (PAN) images is an interesting problem. The problem of fusing a high spectral and low spatial resolution image with an auxiliary image of higher spatial but lower spectral resolution, also known as multi-resolution image fusion, has been explored for many years [AMV+11]. From an application point of view, this problem is also important as motivated by recent national programs, e.g., the Japanese next-generation space-borne hyperspectral image suite (HISUI), which fuses co-registered MS and HS images acquired over the same scene under the same conditions [YI13]. Bayesian fusion allows for an intuitive interpretation of the fusion process via the posterior distribution. Since the fusion problem is usually ill-posed, the Bayesian methodology offers a convenient way to regularize the problem by defining appropriate prior distribution for the scene of interest. The aim of this thesis is to study new multi-band image fusion algorithms to enhance the resolution of hyperspectral image. In the first chapter, a hierarchical Bayesian framework is proposed for multi-band image fusion by incorporating forward model, statistical assumptions and Gaussian prior for the target image to be restored. To derive Bayesian estimators associated with the resulting posterior distribution, two algorithms based on Monte Carlo sampling and optimization strategy have been developed. In the second chapter, a sparse regularization using dictionaries learned from the observed images is introduced as an alternative of the naive Gaussian prior proposed in Chapter 1. instead of Gaussian prior is introduced to regularize the ill-posed problem. Identifying the supports jointly with the dictionaries circumvented the difficulty inherent to sparse coding. To minimize the target function, an alternate optimization algorithm has been designed, which accelerates the fusion process magnificently comparing with the simulation-based method. In the third chapter, by exploiting intrinsic properties of the blurring and downsampling matrices, a much more efficient fusion method is proposed thanks to a closed-form solution for the Sylvester matrix equation associated with maximizing the likelihood. The proposed solution can be embedded into an alternating direction method of multipliers or a block coordinate descent method to incorporate different priors or hyper-priors for the fusion problem, allowing for Bayesian estimators. In the last chapter, a joint multi-band image fusion and unmixing scheme is proposed by combining the well admitted linear spectral mixture model and the forward model. The joint fusion and unmixing problem is solved in an alternating optimization framework, mainly consisting of solving a Sylvester equation and projecting onto a simplex resulting from the non-negativity and sum-to-one constraints. The simulation results conducted on synthetic and semi-synthetic images illustrate the advantages of the developed Bayesian estimators, both qualitatively and quantitatively.

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Bieniarz, Jakub. "Sparse Methods for Hyperspectral Unmixing and Image Fusion." Doctoral thesis, 2016. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2016030214286.

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In recent years, the substantial increase in the number of spectral channels in optical remote sensing sensors allows more detailed spectroscopic analysis of objects on the Earth surface. Modern hyperspectral sensors are able to sample the sunlight reflected from a target on the ground with hundreds of adjacent narrow spectral channels. However, the increased spectral resolution comes at the price of a lower spatial resolution, e.g. the forthcoming German hyperspectral sensor Environmental Mapping and Analysis Program (EnMAP) which will have 244 spectral channels and a pixel size on ground as large as 30 m x 30 m. The main aim of this thesis is dealing with the problem of reduced spatial resolution in hyperspectral sensors. This is addressed first as an unmixing problem, i.e., extraction and quantification of the spectra of pure materials mixed in a single pixel, and second as a resolution enhancement problem based on fusion of multispectral and hyperspectral imagery. This thesis proposes novel methods for hyperspectral unmixing using sparse approximation techniques and external spectral dictionaries, which unlike traditional least squares-based methods, do not require pure material spectrum selection step and are thus able to simultaneously estimate the underlying active materials along with their respective abundances. However, in previous works it has been shown that these methods suffer from some drawbacks, mainly from the intra dictionary coherence. To improve the performance of sparse spectral unmixing, the use of derivative transformation and a novel two step group unmixing algorithm are proposed. Additionally, the spatial homogeneity of abundance vectors by introducing a multi-look model for spectral unmixing is exploited. Based on the above findings, a new method for fusion of hyperspectral images with higher spatial resolution multispectral images is proposed. The algorithm exploits the spectral information of the hyperspectral image and the spatial information from the multispectral image by means of sparse spectral unmixing to form a new high spatial and spectral resolution hyperspectral image. The introduced method is robust when applied to highly mixed scenarios as it relies on external spectral dictionaries. Both the proposed sparse spectral unmixing algorithms as well as the resolution enhancement approach are evaluated quantitatively and qualitatively. Algorithms developed in this thesis are significantly faster and yield better or similar results to state-of-the-art methods.

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Ahmad, Touseef. "Augmenting Hyperspectral Image Unmixing Models Using Spatial Correlation, Spectral Variability, And Sparsity." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6081.

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Hyperspectral imaging sensors sample sunlight reflected from different targets on Earth's surface by utilising a series of contiguous narrow spectral channels. The higher spectral resolution of hyperspectral images (HSIs) comes at the cost of low spatial resolution; therefore, most pixels may consist of multiple targets. Spectral unmixing algorithms are essential in addressing the issue of low spatial resolution of HSIs by incorporating spatial correlation, spectral variability, and sparsity constraints. Moreover, unmixing methods can be used to measure the fractional abundance of pure materials (called endmembers) in a mixed pixel and are also helpful in enhancing the spatial resolution of HSIs. In the first part of the thesis, sparse unmixing methods were improved by incorporating high adjacency effects and endmember spectral variability. Traditional total-variation-based sparse unmixing methods avoid high adjacency effects among the neighbouring pixels, which leads to over-smoothing and causes errors in the abundance estimation. A four-directional total-variation spatial regularisation approach is proposed to address these issues, which yields robust results when applied to low signal-to-noise-ratio images. Furthermore, spectral unmixing algorithms analyse the HSI by treating endmembers as independent entities in many remote sensing applications such as agriculture or mineral study. Therefore, traditional methods fail to estimate the fractional abundance of endmembers accurately. An endmember variability-based spectral-spatial weighted sparse regression unmixing method is proposed and demonstrated using a real airborne AVIRIS-NG HSI over the agriculture field, where fractional covers of red and black soil were estimated over sparsely vegetated areas. The experimental finding shows promising results as compared to other methods. In the second part, the generalised bilinear mixing (GBM) model-based nonlinear unmixing methods were improved. Real HSIs are usually contaminated with complex mixed noises such as Gaussian noise, dead pixels, stripes, impulse noise, etc. The intensity of mixed noise may also vary band-to-band in HSIs, which reduces the accuracy of traditional GBM-based unmixing methods. A computationally efficient bandwise-GBM model is proposed to deal with these issues. The proposed technique reduces computation time while being comparable (and often better) to traditional GBM-based unmixing methods. Furthermore, traditional GBM-based unmixing approaches also reduce unmixing performance by ignoring spatial correlation among the neighbouring pixels. A super-pixel-guided weighted low-rank representation for the robust GBM model is proposed to overcome the above issues. This model employs an entropy rate superpixel segmentation approach to extract homogenous patches in the HSI that underlie the low-rank property. A weighted nuclear norm minimisation approach is introduced for each homogenous patch to estimate the low-rank property, which allocates smaller weights to larger singular values and higher weights to smaller ones. The proposed method significantly improves the fractional abundance estimation by incorporating spatial correlation and sparse noise constraints in the unmixing model. Finally, spectral unmixing methods are utilised to improve the spatial resolution of HSI by employing high spatial resolution multispectral images (MSIs). Traditional unmixing-based fusion methods avoid noise effects in the modelling, which reduces the accuracy of fusion products. A robust coupled non-negative matrix factorisation is developed for HSI and MSI fusion, incorporating sparse noise effects in the unmixing models of HSI and MSI. Both unmixing problems are coupled by using the sensors' relative spectral response and point spread function. The above study indicates that the proposed methods achieve robust performance by comprising spatial correlation, spectral variability, and sparsity constraints in the unmixing process.

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Nicolae, Aurel. "A comparative analysis of classic Geometrical methods and sparse regression methods for linearly unmixing hyperspectral image data." Thesis, 2019. https://hdl.handle.net/10539/29542.

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A research report submitted to the Faculty of Science, University of Witwatersrand, Johannesburg, in the ful lment of the requirements for the degree of Masters of Science by Coursework and Research Report, 2019
This research report presents an across-the-board comparative analysis on algorithms for linearly unmixing hyperspectral image data cubes. Convex geometry based endmember extraction algorithms (EEAs) such as the pixel purity index (PPI) algorithm and N-FINDR have been commonly used to derive the material spectral signatures called endmembers from the hyperspectral images. The estimation of their corresponding fractional abundances is done by solving the related inverse problem in a least squares sense. Semi-supervised sparse regression algorithms such as orthogonal matching pursuit (OMP) and sparse unmixing algorithm via variable splitting and augmented Lagrangian (SUnSAL) bypass the endmember extraction process by employing widely available spectral libraries a priori, automatically returning the fractional abundances and sparsity estimates. The main contribution of this work is to serve as a rich resource on hyperspectral image unmixing, providing end-to-end evaluation of a wide variety of algorithms using di erent arti cial data sets.
XN2020

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Book chapters on the topic "Sparse hyperspectral unmixing"

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Zhang, Shaoquan, Yuanchao Su, Xiang Xu, Jun Li, Chengzhi Deng, and Antonio Plaza. "Recent Advances in Hyperspectral Unmixing Using Sparse Techniques and Deep Learning." In Hyperspectral Image Analysis, 377–405. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38617-7_13.

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Wu, Feiyang, Yuhui Zheng, and Le Sun. "Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior." In Intelligence Science and Big Data Engineering. Visual Data Engineering, 506–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36189-1_42.

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Marques, Ion, and Manuel Graña. "Hybrid Sparse Linear and Lattice Method for Hyperspectral Image Unmixing." In Lecture Notes in Computer Science, 266–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07617-1_24.

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Liu, Jing, You Zhang, Xiao-die Yang, and Yi Liu. "Hyperspectral Remote Sensing Images Unmixing Based on Sparse Concept Coding." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, 823–30. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_89.

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Zenati, Tarek, Bruno Figliuzzi, and Shu Hui Ham. "Surface Oxide Detection and Characterization Using Sparse Unmixing on Hyperspectral Images." In Lecture Notes in Computer Science, 291–302. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13321-3_26.

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Esmaeili Salehani, Yaser, and Mohamed Cheriet. "Non-dictionary Aided Sparse Unmixing of Hyperspectral Images via Weighted Nonnegative Matrix Factorization." In Lecture Notes in Computer Science, 596–604. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59876-5_66.

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Chen, Mengyue, Fanqiang Kong, Shunmin Zhao, and Keyao Wen. "Hyperspectral Unmixing Method Based on the Non-convex Sparse and Spatial Correlation Constraints." In Lecture Notes in Electrical Engineering, 441–46. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8411-4_61.

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Li, Denggang, Shutao Li, and Huali Li. "Hyperspectral Image Unmixing Based on Sparse and Minimum Volume Constrained Nonnegative Matrix Factorization." In Communications in Computer and Information Science, 44–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45643-9_5.

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Conference papers on the topic "Sparse hyperspectral unmixing"

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Sigurdsson, Jakob, Magnus O. Ulfarsson, Johannes R. Sveinsson, and Jose M. Bioucas-Dias. "Sparse distributed hyperspectral unmixing." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7730824.

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Iordache, Marian-Daniel, Jose Bioucas-Dias, and Antonio Plaza. "Unmixing sparse hyperspectral mixtures." In 2009 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009). IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5417368.

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Aggarwal, Hemant Kumar, and Angshul Majumdar. "Sparse filtering based hyperspectral unmixing." In 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2016. http://dx.doi.org/10.1109/whispers.2016.8071765.

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Iordache, Marian-Daniel, Jose M. Bioucas-Dias, and Antonio Plaza. "Collaborative sparse unmixing of hyperspectral data." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6351900.

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Rodriguez Alves, Jose M., Jose M. P. Nascimento, Jose M. Bioucas-Dias, Antonio Plaza, and Vitor Silva. "Parallel sparse unmixing of hyperspectral data." In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6723057.

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Iordache, Marian-Daniel, Antonio Plaza, and Jose Bioucas-Dias. "Recent developments in sparse hyperspectral unmixing." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5653075.

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Ma, Yong, Chang Li, and Jiayi Ma. "Robust sparse unmixing of hyperspectral data." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7730618.

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Sigurdsson, Jakob, Magnus O. Ulfarsson, and Johannes R. Sveinsson. "Sparse and low rank hyperspectral unmixing." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8126936.

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Chen, Yonggang, Chengzhi Deng, Shaoquan Zhang, Fan Li, Ningyuan Zhang, and Shengqian Wang. "Dual Spatial Weighted Sparse Hyperspectral Unmixing." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883616.

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Fang, Bei, Ying Li, Peng Zhang, and Bendu Bai. "Kernel sparse NMF for hyperspectral unmixing." In 2014 IEEE International Conference on Orange Technologies (ICOT). IEEE, 2014. http://dx.doi.org/10.1109/icot.2014.6954672.

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Reports on the topic "Sparse hyperspectral unmixing"

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Moeller, Michael, Ernie Esser, Stanley Osher, Guillermo Sapiro, and Jack Xin. A Convex Model for Matrix Factorization and Dimensionality Reduction on Physical Space and Its Application to Blind Hyperspectral Unmixing. Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada540658.

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