Filters








291 Hits in 4.9 sec

Hyperspectral Unmixing with Robust Collaborative Sparse Regression

Chang Li, Yong Ma, Xiaoguang Mei, Chengyin Liu, Jiayi Ma
2016 Remote Sensing  
In this paper, we propose a new method named robust collaborative sparse regression (RCSR) based on the robust LMM (rLMM) for hyperspectral unmixing.  ...  Moreover, Iordache et al. proposed collaborative SUnSAL (CLSUnSAL) [18] , which improves the unmixing results by adopting the collaborative (also called "multitask" or "simultaneous") sparse regression  ...  as a linear sparse regression problem.  ... 
doi:10.3390/rs8070588 fatcat:bhikaob3yjhxhb4udqpzdfmgre

Collaborative Sparse Regression for Hyperspectral Unmixing

Marian-Daniel Iordache, Jose M. Bioucas-Dias, Antonio Plaza
2014 IEEE Transactions on Geoscience and Remote Sensing  
Index Terms-Collaborative sparse regression, hyperspectral imaging, sparse unmixing, spectral libraries.  ...  Our experimental results with both synthetic and real hyperspectral data sets show clearly the advantages obtained using the new joint sparse regression strategy, compared with the pixelwise independent  ...  Green and Dr. Roger N. Clark for, respectively sharing the AVIRIS Cuprite data and the USGS spectral library with the scientific community.  ... 
doi:10.1109/tgrs.2013.2240001 fatcat:2urelufdtrgpldh25viykyzoui

An Overview on Linear Unmixing of Hyperspectral Data

Jiaojiao Wei, Xiaofei Wang
2020 Mathematical Problems in Engineering  
factorization (NMF), Bayesian method, and sparse unmixing.  ...  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  ...  [13] proposed a spectral unmixing resolution using extended support vector machines. Wang et al.  ... 
doi:10.1155/2020/3735403 fatcat:ijkjzzp6lbavhfhkyx7rwnxngy

MUSIC-CSR: Hyperspectral Unmixing via Multiple Signal Classification and Collaborative Sparse Regression

Marian-Daniel Iordache, Jose M. Bioucas-Dias, Antonio Plaza, Ben Somers
2014 IEEE Transactions on Geoscience and Remote Sensing  
The second step applies collaborative sparse regression,  ...  In recent years, sparse unmixing has been widely used as a reliable spectral unmixing methodology.  ...  Linear spectral unmixing has been recently addressed under a sparse regression framework [5] , [6] .  ... 
doi:10.1109/tgrs.2013.2281589 fatcat:7llbkr5vijh23btguqz2qhxoym

Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery

Ruyi Feng, Lizhe Wang, Yanfei Zhong
2018 Remote Sensing  
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  ...  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  ...  Li and J. Ma for sharing their latest sparse unmixing algorithm source code and their good suggestions as to how we could improve our paper.  ... 
doi:10.3390/rs10101546 fatcat:xwd7iifshnfnned4v6eny73gge

Collaborative sparse unmixing of hyperspectral data

Marian-Daniel Iordache, Jose M. Bioucas-Dias, Antonio Plaza
2012 2012 IEEE International Geoscience and Remote Sensing Symposium  
Sparse unmixing aims at estimating the constituent materials (endmembers) and their respective fractional abundances in each pixel of a hyperspectral image by assuming that the endmembers are present in  ...  The experimental results, obtained with both simulated and real data, confirm the potential of the proposed approach in the unmixing problem.  ...  Section 3 introduces a refinement of the sparse unmixing problem and briefly describes other spectral unmixing techniques used for comparative purposes in this work.  ... 
doi:10.1109/igarss.2012.6351900 dblp:conf/igarss/IordacheBP12 fatcat:ef74x4di5nfwln5kxi7r5hbfyi

Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing [article]

Yoann Altmann and Marcelo Pereyra and Jose Bioucas-Dias
2015 arXiv   pre-print
This paper presents a new Bayesian collaborative sparse regression method for linear unmixing of hyperspectral images.  ...  Our contribution is twofold; first, we propose a new Bayesian model for structured sparse regression in which the supports of the sparse abundance vectors are a priori spatially correlated across pixels  ...  CONCLUSIONThis paper presented a new Bayesian method for linear unmixing of hyperspectral image that is based on a collaborative sparse regression formulation.  ... 
arXiv:1409.8129v2 fatcat:b5vp3dtxp5h7jcpazs6cae5vxa

Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted Low-Rank Representation for Hyperspectral Image Unmixing

Hongjun Su, Cailing Jia, Pan Zheng, Qian Du
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In the method, the weighted collaborative sparse regression explores the pixels shared the same support set to help the sparsity of abundance fraction, and the reweighted low rank representation minimizes  ...  In order to address these prominent problems, a new paradigm to characterize sparse hyperspectral unmixing is proposed, namely, the superpixel-based weighted collaborative sparse regression and reweighted  ...  In this paper, a new approach of sparse spectral unmixing algorithm based on superpixel weighted collaborative sparse regression and reweighted low rank representation for hyperspectral image unmixing  ... 
doi:10.1109/jstars.2021.3133428 fatcat:pbbag6efkjdphgswkyz22x6ch4

Collaborative sparse regression using spatially correlated supports - Application to hyperspectral unmixing

Yoann Altmann, Marcelo Pereyra, Jose Bioucas-Dias
2015 IEEE Transactions on Image Processing  
Collaborative sparse regression using spatially correlated supports -Application to hyperspectral unmixing.  ...  Jean-Yves Tourneret and Dr Nicolas Dobigeon, from the University of Toulouse, IRIT-ENSEEIHT, France, for interesting discussion regarding this work.  ...  CONCLUSION This paper presented a new Bayesian method for linear unmixing of hyperspectral image that is based on a collaborative sparse regression formulation.  ... 
doi:10.1109/tip.2015.2487862 pmid:26452285 fatcat:wzebgreydzdphi4fzpnzszuhj4

Dictionary pruning in sparse unmixing of hyperspectral data

Marian-Daniel Iordache, Jose M. Bioucas-Dias, Antonio Plaza
2012 2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS)  
When hyperspectral unmixing relies on the use of spectral libraries (dictionaries of pure spectra), the sparse regression problem to be solved is severely ill-conditioned and time-consuming.  ...  decrease the running time of the sparse unmixing algorithm.  ...  INTRODUCTION Linear spectral unmixing has been recently addressed under a sparse regression framework [1] , [2] .  ... 
doi:10.1109/whispers.2012.6874329 dblp:conf/whispers/IordacheBP12 fatcat:ouy3zhj2nrccvfjltvasixwswa

Joint-Sparse-Blocks Regression for Total Variation Regularized Hyperspectral Unmixing

Jie Huang, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng
2019 IEEE Access  
Simulated and real-data experiments show the advantages of the proposed algorithm. INDEX TERMS Hyperspectral images, spectral unmixing, total variation regularization, joint-sparse-blocks regression.  ...  To this end, we include the joint-sparse-blocks regression into the TV spatial regularization framework and present a new unmixing algorithm, termed joint-sparse-blocks unmixing via variable splitting  ...  The collaborative sparse regression framework has been presented in [26] to encourage that all pixels in the data set share the same support set.  ... 
doi:10.1109/access.2019.2943110 fatcat:dewrwniyzzbopey2mqnepdlgbi

Fast Hyperspectral Unmixing via Reweighted Sparse Regression

Hongwei HAN, Ke GUO, Maozhi WANG, Tingbin ZHANG, Shuang ZHANG
2019 IEICE transactions on information and systems  
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.  ...  Acknowledgments This study is financially supported by the National Key  ... 
doi:10.1587/transinf.2018edp7374 fatcat:skpwmrpaf5dqdoqm3g5yvtqiye

Hyperspectral band selection using a collaborative sparse model

Qian Du, Jose M. Bioucas-Dias, Antonio Plaza
2012 2012 IEEE International Geoscience and Remote Sensing Symposium  
In this paper, we propose to use a collaborative sparse model for further improvement.  ...  It not only requires that the linear regression coefficients are sparse, but also requires that the same set of active bands is shared by all the bands to be removed.  ...  The basic idea is to use the fast N-FINDR+LP method to do band pre-selection, and then apply an efficient linear sparse regression (SR) technique to refine the band selection result.  ... 
doi:10.1109/igarss.2012.6350781 dblp:conf/igarss/DuBP12 fatcat:oo2t6nby2nchldcn37yfqkbnha

Double Regression-Based Sparse Unmixing for Hyperspectral Images

Shuaiyang Zhang, Wenshen Hua, Gang Li, Jie Liu, Fuyu Huang, Qianghui Wang, Penghai Wu
2021 Journal of Sensors  
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.  ...  Acknowledgments This research was supported by the Department of Electronic and Optical Engineering, Shijiazhuang Campus, AEU.  ... 
doi:10.1155/2021/5575155 fatcat:fjby6vo7nbezbmy64w5iulahwa

Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior

Zheng, Wu, Shim, Sun
2019 Remote Sensing  
In addition, the local spatial information and spectral characteristic are also taken into account by introducing TV regularization and collaborative sparse terms, respectively.  ...  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  ...  Acknowledgements: The authors would like to thank the editors and three anonymous reviewers for their constructive comments on our manuscript, which makes our work much better.  ... 
doi:10.3390/rs11242897 fatcat:eah3airicbccfepbdrslgncuxu
« Previous Showing results 1 — 15 out of 291 results