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Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing
2021
Remote Sensing
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In this paper, we propose two gated autoencoder networks with the intention of adaptively controlling the contribution of spectral and spatial features in unmixing process. ...
This study confirms the effectiveness of gating mechanism in improving the accuracy and efficiency of utilizing spatial signatures for spectral unmixing. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/rs13163147
fatcat:snphfepgn5af5pxbjnb2dhqye4
FPGA Implementation of Abundance Estimation for Spectral Unmixing of Hyperspectral Data Using the Image Space Reconstruction Algorithm
2012
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Due to the high dimensionality of the hyperspectral data, spectral unmixing is a very time-consuming task. ...
One of the most popular and widely used techniques for analyzing remotely sensed hyperspectral data is spectral unmixing, which relies on two stages: (i) identification of pure spectral signatures (endmembers ...
The authors also would like to take this opportunity to gratefully thank the three anonymous reviewers for providing outstanding comments which helped us improve the technical quality and presentation of ...
doi:10.1109/jstars.2011.2171673
fatcat:ek4cunarxjetfoglmqpk7yxzjq
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model
[article]
2020
arXiv
pre-print
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In this paper, spectral variation is considered from a physics-based approach and incorporated into an end-to-end spectral unmixing algorithm via differentiable programming. ...
Then, this dispersion model is utilized as a generative model within an analysis-by-synthesis spectral unmixing algorithm. ...
Appendix D: Implementation Details Fully Constrained Least Squares We compare against Fully Constrained Least Squares (FCLS) [28] which is a popular classical unmixing algorithm. ...
arXiv:2007.05996v1
fatcat:dxi736szvjbzlitnksph4wlxxu
A Stepwise Analytical Projected Gradient Descent Search for Hyperspectral Unmixing and Its Code Vectorization
2017
IEEE Transactions on Geoscience and Remote Sensing
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Its performance was compared to the commonly used fully constrained least squares unmixing (FCLSU), the generalized bilinear model (GBM) method for hyperspectral unmixng, and the fast state-of-the-art ...
We present in this paper a new methodology for spectral unmixing, where a vector of fractions, corresponding to a set of endmembers (EMs), is estimated for each pixel in the image. ...
This results in a considerably more efficient performance of the fully constrained spectral unmixing proposed. ...
doi:10.1109/tgrs.2017.2692999
fatcat:axzkktuv5bhj5mdqvxgkxkgvge
Real-time implementation of remotely sensed hyperspectral image unmixing on GPUs
2012
Journal of Real-Time Image Processing
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In this paper, we develop the first real-time implementation of a full spectral unmixing chain in commodity graphics processing units (GPUs). ...
The spectral unmixing process allows sub-pixel analysis of hyperspectral images, but can be computationally expensive due to the high dimensionality of the data. ...
been supported by the European Community's Marie Curie Research Training Networks Programme under reference MRTN-CT-2006-035927, Hyperspectral Imaging Network (HYPER-I-NET), and by the Spanish Ministry of ...
doi:10.1007/s11554-012-0269-2
fatcat:gfduhbbdebb4bbcjre2vbce2mq
Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization
2006
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
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In hyperspectral imaging, hundreds of images are taken at narrow and contiguous spectral bands providing us with high spectral resolution spectral signatures that can be used to discriminate between objects ...
This research dealt with the unsupervised determination of the constituents and their fractional abundance in each pixel in a hyperspectral image using a constrained positive matrix factorization (cPMF ...
Algorithms to solve the fully constrained abundance estimation problem have been proposed in [2, 15] . ...
doi:10.1117/12.667976
fatcat:yj3xl6cyqjg2ze4jtnuxdkymzi
Hyperspectral Unmixing on Multicore DSPs: Trading Off Performance for Energy
2014
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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In this paper, we analyze the acceleration of spectral unmixing, a key technique to process hyperspectral images, on multicore architectures. ...
Wider coverage of observation missions will increase onboard power restrictions while, at the same time, pose higher demands from the perspective of processing time, thus asking for the exploration of ...
We thank TI for the donation of the DSP processor used in the experimental section. ...
doi:10.1109/jstars.2013.2266927
fatcat:wku6xdgv6vcgpgkrlchrd7p6su
Sparse Unmixing for Hyperspectral Imagery via Comprehensive-Learning-based Particle Swarm Optimization
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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With the use of the extracted endmembers, supervised abundance inversion algorithms such as the fully constrained least squares (FCLS) proposed by Heinz et al. ...
Table XI shows the execution time of eight algorithms for unmixing three real hyperspectral images. ...
doi:10.1109/jstars.2021.3115177
fatcat:kvpbzduq2reofpablsalvkguqm
A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data
2004
IEEE Transactions on Geoscience and Remote Sensing
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His main research interests span computer vision, image processing, pattern recognition, and development and efficient implementation of hyperspectral image analysis algorithms on massively parallel computing ...
Plaza is currently serving as a reviewer for the IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING and IEEE TRANSACTIONS ON IMAGE PROCESSING. ...
between two different algorithms: fully constrained (FCLSU) and unconstrained (ULSU) linear spectral unmixing. ...
doi:10.1109/tgrs.2003.820314
fatcat:imlontyyb5espcloxarngoxuwu
The Promise of Reconfigurable Computing for Hyperspectral Imaging Onboard Systems: A Review and Trends
2013
Proceedings of the IEEE
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Fast processing solutions for compression and/or interpretation of hyperspectral data onboard spacecraft imaging platforms are discussed in this paper with the purpose of giving a more efficient exploitation ...
ABSTRACT | Hyperspectral imaging is an important technique in remote sensing which is characterized by high spectral resolutions. ...
Acknowledgment The authors would like to thank the Guest Editors of this special issue for their very kind invitation to provide a contribution, as well as the three anonymous reviewers for their outstanding ...
doi:10.1109/jproc.2012.2231391
fatcat:aepzokz6wne2dbxtlx3ij5sqau
Multiple Clustering Guided Nonnegative Matrix Factorization for Hyperspectral Unmixing
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Recently, constrained nonnegative matrix factorization (NMF) has been demonstrated to be a powerful tool for spectral unmixing. ...
Specifically, in order to provide selfsupervised information to guide the NMF-based unmixing model, multiple clustering is integrated into the optimization process of NMF. ...
the fully constrained least squares (FCLS) [53] algorithm. ...
doi:10.1109/jstars.2020.3020541
fatcat:ccbmoy4vzvfoneriuhh43jux54
Improved Local Spectral Unmixing of hyperspectral data using an algorithmic regularization path for collaborative sparse regression
2017
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Local Spectral Unmixing (LSU) methods perform the unmixing of hyperspectral data locally in regions of the image. ...
Results on real data show the interest of the proposed approach. ...
the number of spectral bands, and the number of pixels in the region, respectively). ...
doi:10.1109/icassp.2017.7953346
dblp:conf/icassp/DrumetzTVCJ17
fatcat:twkrewsp2jav5epek62sh5qsu4
A new tool for variable multiple endmember spectral mixture analysis (VMESMA)
2005
International Journal of Remote Sensing
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Spectral mixture analysis is a widely used method to determine the sub-pixel abundance of vegetation, soils and other spectrally distinct materials that fundamentally contribute to the spectral signal ...
Based on an iterative feedback process, the unmixing performance may be improved at each stage until an optimum level is reached. ...
N The covariance matrix of the observations, e.g. to modify the relative contribution of each specific spectral band in the unmixing analysis (see §2.7). ...
doi:10.1080/01431160512331337817
fatcat:6zytol2cljgirpsldqsvgtuzr4
Progress in Hyperspectral Remote Sensing Science and Technology in China Over the Past Three Decades
2014
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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These tools such as the Hyperspectral Image Processing and Analysis System (HIPAS) incorporate a number of special algorithms and features designed to take advantage of the wealth of information contained ...
This paper reviews progress in hyperspectral remote sensing (HRS) in China, focusing on the past three decades. ...
Like the NMM, the main tasks for linear spectral unmixing include endmember determination and abundance estimation. ...
doi:10.1109/jstars.2013.2267204
fatcat:yu4pp5zyqzhafa4ec7m3vva7xi
A Novel Hierarchical Bayesian Approach for Sparse Semisupervised Hyperspectral Unmixing
2012
IEEE Transactions on Signal Processing
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In this paper the problem of semisupervised hyperspectral unmixing is considered. ...
More specifically, the unmixing process is formulated as a linear regression problem, where the abundance's physical constraints are taken into account. ...
Under these constraints, spectral unmixing is formulated as a convex optimization problem, which can be addressed using iterative methods, e.g., the fully constrained least squares method, [7] , or numerical ...
doi:10.1109/tsp.2011.2174052
fatcat:e4ahhbglyzfwnnmiex5tydx6ba
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