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Spectral-spatial joint sparsity unmixing of hyperspectral data using overcomplete dictionaries

J. Bieniarz, E. Aguilera, X. X. Zhu, R. Muller, U. Heiden, P. Reinartz
2014 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)  
Index Terms-Spectral unmixing, joint sparsity, overcomplete spectral dictionary.  ...  To demonstrate the efficiency of our framework, we perform experiments using both simulated and real hyperspectral data.  ...  Simulated Scenario In order to assess the performance of the MLJSR algorithm with hyperspectral data using the dictionary A, we simulated 10000 joint Y L pixel ensembles each with L = 4 pixels.  ... 
doi:10.1109/whispers.2014.8077639 dblp:conf/whispers/BieniarzAZMHR14 fatcat:wtkxofundzeyfalbniscqbtali

Joint Sparsity Model for Multilook Hyperspectral Image Unmixing

J. Bieniarz, E. Aguilera, X. X. Zhu, R. Muller, P. Reinartz
2015 IEEE Geoscience and Remote Sensing Letters  
Index Terms-Joint sparsity, overcomplete spectral dictionary, spectral unmixing.  ...  Recent work on hyperspectral image (HSI) unmixing has addressed the use of overcomplete dictionaries by employing sparse models.  ...  Heiden for providing the roof spectral library.  ... 
doi:10.1109/lgrs.2014.2358623 fatcat:3oxpca2rk5aotm2paneisnunlm

Jointly sparse fusion of hyperspectral and multispectral imagery

Claas Grohnfeldt, Xiao Xiang Zhu, Richard Bamler
2013 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS  
First experimental results using airborne HySpex hyperspectral data and synthesized WorldView-2 imagery are presented.  ...  In this paper we apply the recently proposed J-SparseFI data fusion method to the fusion of a low-resolution hyperspectral image and a high-resolution multispectral image.  ...  data, and (c) highresolution hyperspectral data reconstructed using J-SparseFI.  ... 
doi:10.1109/igarss.2013.6723732 dblp:conf/igarss/GrohnfeldtZB13 fatcat:ttas4lw4xjed5oycsh3m2xnu7u

A blind spectral unmixing in wavelet domain

Vijayashekhar Ss, Jignesh Shashikant Bhatt
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Spectrally dense and overlapped hyperspectral data is represented using biorthogonal wavelet bases that yield a compact linear mixing model in the wavelet domain.  ...  ., the logarithm of determinant volume regularizer enforces minimum endmember simplex, smoothness (spatial) prior to individual abundance maps, and spectral constraint through learning dictionary of abundances  ...  We acknowledge the USGS for the spectral library of minerals. Authors are thankful to Indian Institute of Information Technology Vadodara, India for funding this research work. Thanks to Dr.  ... 
doi:10.1109/jstars.2021.3116698 fatcat:hzp5rgv7l5hs7btztvurst5ggi

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

Jie Huang, Ting-Zhu Huang, Xi-Le Zhao, Liang-Jian Deng
2019 IEEE Access  
INDEX TERMS Hyperspectral images, spectral unmixing, total variation regularization, joint-sparse-blocks regression. FIGURE 6.  ...  It is therefore hoped to impose joint sparsity, instead of classic single sparsity, on these adjacent pixels to further improve the unmixing performance.  ...  Based on the overcomplete spectral dictionary, the collaborative (also called ''joint'' or ''row'') sparse regression framework assumes that pixels in a region share the same support set of endmembers.  ... 
doi:10.1109/access.2019.2943110 fatcat:dewrwniyzzbopey2mqnepdlgbi

Exploiting sparsity in remote sensing and earth observation: Theory, applications and future trends

Xiao Xiang Zhu, Richard Bamler
2015 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
Along with the significant development of the compressive sensing theory, exploitation of sparsity in remote sensing became a very relevant and active field.  ...  Tailored to this special session, this tutorial gives a review, to the best knowledge of the session chair, on recent advances in sparsity exploitation in remote sensing and Earth observation, regarding  ...  Sufficiently small images patches normally have a sparse representation in overcomplete dictionaries trained from the data. − Spectral unmixing for hyperspectral data [29] - [31] : The goal of spectral  ... 
doi:10.1109/igarss.2015.7326406 dblp:conf/igarss/ZhuB15 fatcat:vyokxlbjavgkjbl2thma7f77fe

Joint Anomaly Detection and Spectral Unmixing for Planetary Hyperspectral Images

Sina Nakhostin, Harold Clenet, Thomas Corpetti, Nicolas Courty
2016 IEEE Transactions on Geoscience and Remote Sensing  
Among classical problems in the analysis of hyperspectral images, a crucial one is unsupervised non-linear spectral unmixing, which aims at estimating the spectral signatures of elementary materials and  ...  Hyperspectral images are commonly used in the context of planetary exploration, especially for the analysis of the composition of planets.  ...  Also we would like to thank the authors in [7] , [22] , [48] for providing us the codes of their algorithms, which made the comparisons possible within this work.  ... 
doi:10.1109/tgrs.2016.2586188 fatcat:3ue4hiqwwrecdcx7jnkyw7jifi

Spatial resolution enhancement of hyperspectral images based on redundant dictionaries

Suyu Wang, Bo Wang, Zongxiang Zhang
2015 Journal of Applied Remote Sensing  
Spatial resolution enhancement of hyperspectral images is one of the key and difficult topics in the field of imaging spectrometry.  ...  Experimental results show that the pixel curve based sparse representation is more suitable for a hyperspectral image; the highly spectral correlations are better used for resolution enhancement.  ...  Acknowledgments This work is supported by the National Natural Science Foundation of China (No. 61201361), Science Foundation of the Beijing Education Commission (KM201310005028), Training Programme Foundation  ... 
doi:10.1117/1.jrs.9.097492 fatcat:dtwwiwpfancbplm3gkof3p447q

A regularized sparse approximation method for hyperspectral image classification

Leila Belmerhnia, El-Hadi Djermoune, David Brie, Cedric Carteret
2016 2016 IEEE Statistical Signal Processing Workshop (SSP)  
This approach is applied to a wood wastes classification problem using NIR hyperspectral images.  ...  The proposed approach consists in formulating the problem as a convex multi-objective optimization problem which incorporates a term favoring the simultaneous sparsity of the estimated coefficients and  ...  It consists in representing a signal using a minimum number of vectors from an overcomplete dictionary.  ... 
doi:10.1109/ssp.2016.7551846 dblp:conf/ssp/BelmerhniaDBC16 fatcat:2sbst4ps2jdfreouybf2ybye4u

Hyperspectral Image Resolution Enhancement Approach Based on Local Adaptive Sparse Unmixing and Subpixel Calibration

Yidan Teng, Ye Zhang, Chunli Ti, Junping Zhang
2018 Remote Sensing  
Unmixing based fusion aims at generating a high spectral-spatial resolution image (HSS) with the same surface features of the high spatial resolution multispectral image (MS) and low spatial resolution  ...  First, we put forward a local adaptive sparse unmixing based fusion (LASUF) algorithm, in which the sparsity of the abundance matrices is appended as the constraint to the optimization fusion, considering  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs10040592 fatcat:3lskz6ognbfuze3dav5vjdo5ua

S3CRF: Sparse Spatial-Spectral Conditional Random Field Target Detection Framework for Airborne Hyperspectral Data

Shaoyu Wang, Yanfei Zhong, Ji Zhao, Xinyu Wang, Xin Hu
2020 IEEE Access  
Airborne hyperspectral data have both high spectral and spatial resolutions.  ...  However, few of the current spatial-spectral target detection methods can fully exploit the spatial information while solving the spectral variability problem.  ...  STD and SASTD solve the spectral variability problem, to some extent, by constructing the overcomplete dictionary and using sparse coding, in which STD does not use the spatial information.  ... 
doi:10.1109/access.2020.2978586 fatcat:rx7r6kowfjhpjehesenjkodbbm

Hyperspectral Remote Sensing Data Analysis and Future Challenges

Jose M. Bioucas-Dias, Antonio Plaza, Gustavo Camps-Valls, Paul Scheunders, Nasser Nasrabadi, Jocelyn Chanussot
2013 IEEE Geoscience and Remote Sensing Magazine  
This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection  ...  The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement  ...  A detailed overview of recent advances in spatial-spectral classification of hyperspectral data is available at [102] .  ... 
doi:10.1109/mgrs.2013.2244672 fatcat:4tk7q6izd5hevhnrck36i5wkiy

Blind Source Separation: The Sparsity Revolution [chapter]

Jerome Bobin, Jean-Luc Starck, Yassir Moudden, Mohamed Jalal Fadili
2008 Advances in Imaging and Electron Physics  
This paper overviews a sparsity-based BSS method coined Generalized Morphological Component Analysis (GMCA) that takes advantages of both morphological diversity and sparsity, using recent sparse overcomplete  ...  In remote sensing applications, the specificity of hyperspectral data should be accounted for. We extend the proposed GMCA framework to deal with hyperspectral data.  ...  This distribution provides us with a convenient and formal expression for our prior knowledge of the sparsity of both a k and s k in dictionaries of spectral and spatial waveforms and of the multiplicative  ... 
doi:10.1016/s1076-5670(08)00605-8 fatcat:ofyrkb2mnjhjng6iwa6qv3gmce

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 algorithm exploits the usual low dimensionality of the hyperspectral data sets.  ...  In recent years, sparse unmixing has been widely used as a reliable spectral unmixing methodology.  ...  ) subset of spectral signatures selected from a large (usually overcomplete) library.  ... 
doi:10.1109/tgrs.2013.2281589 fatcat:7llbkr5vijh23btguqz2qhxoym

Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles

Benqin Song, Jun Li, Mauro Dalla Mura, Peijun Li, Antonio Plaza, Jose M. Bioucas-Dias, Jon Atli Benediktsson, Jocelyn Chanussot
2014 IEEE Transactions on Geoscience and Remote Sensing  
Specifically, we use extended multiattribute profiles (EMAPs) to integrate the spatial and spectral information contained in the data.  ...  Our experiments reveal that the proposed approach exploits the inherent low-dimensional structure of the EMAPs to provide state-of-the-art classification results for different multi/hyperspectral data  ...  Landgrebe for making the AVIRIS Indian Pines hyperspectral data set available to the community and Prof. P.  ... 
doi:10.1109/tgrs.2013.2286953 fatcat:oli53omnlfhkrjdiwgh6dkh7qy
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