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2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
Wang, Y., +, Exploiting Block-Sparsity for Hyperspectral Kronecker Compressive Sensing: A Tensor-Based Bayesian Method.  ...  Lu, C., +, TIP 2020 768-781 Hyperspectral imaging A Blind Multiscale Spatial Regularization Framework for Kernel-Based Spectral Unmixing.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Table of contents

2020 IEEE Transactions on Image Processing  
Xia 4911 A Blind Multiscale Spatial Regularization Framework for Kernel-Based Spectral Unmixing ............................. ...................................................... R. A. Borsoi, T.  ...  Lam Phung 4598 Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry .......................... ........................................................... L.  ... 
doi:10.1109/tip.2019.2940373 fatcat:i7hktzn4wrfz5dhq7hj75u6esa

2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12

2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS Dec. 2019 5310-5320 Image enhancement A Robust Pansharpening Algorithm Based on Convolutional Sparse Coding for Spatial Enhancement.  ...  ., +, JSTARS Dec. 2019 4813-4828 A Robust Pansharpening Algorithm Based on Convolutional Sparse Coding for Spatial Enhancement.  ... 
doi:10.1109/jstars.2020.2973794 fatcat:sncrozq3fjg4bgjf4lnkslbz3u

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Pedram Ghamisi, Naoto Yokoya, Jun Li, Wenzhi Liao, Sicong Liu, Javier Plaza, Behnood Rasti, Antonio Plaza
2017 IEEE Geoscience and Remote Sensing Magazine  
This paper offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality  ...  reduction, resolution enhancement, hyperspectral image denoising and restoration, change detection, and fast computing.  ...  and Data Fusion Technical Committee for organizing the 2013 Data Fusion Contest.  ... 
doi:10.1109/mgrs.2017.2762087 fatcat:6ezzye7yyvacbouduqv2f2c7gi

Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms [article]

Yanna Bai, Wei Chen, Jie Chen, Weisi Guo
2020 arXiv   pre-print
In this paper, we present a comprehensive survey of the recent progress in the development of deep learning for solving various linear inverse problems.  ...  Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance  ...  learning for hyperspectral image denoising [52] , and Xin et al. jointly optimize the sensing matrix and sparsifying dictionary for tensor CS [53] .  ... 
arXiv:2007.13290v2 fatcat:kqoerts77nftbl32fctx3za2me

Image Restoration for Remote Sensing: Overview and Toolbox [article]

Benhood Rasti, Yi Chang, Emanuele Dalsasso, Loïc Denis, Pedram Ghamisi
2021 arXiv   pre-print
Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes.  ...  We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic  ...  [123] presented a tensor image decomposition framework where the ℓ 2,1 norm is used to accommodate the column-wise group sparsity of the stripe.  ... 
arXiv:2107.00557v2 fatcat:adn5fpdza5h4tbsycg7yw6rqzu

Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep [article]

Behnood Rasti, Danfeng Hong, Renlong Hang, Pedram Ghamisi, Xudong Kang, Jocelyn Chanussot, Jon Atli Benediktsson
2020 arXiv   pre-print
) for accurate analysis of hyperspectral images.  ...  This article outlines the advances in feature extraction approaches for hyperspectral imagery by providing a technical overview of the state-of-the-art techniques, providing useful entry points for researchers  ...  Melba Crawford for providing the Indian Pines 2010 Data and the National Center for Airborne Laser Mapping (NCALM), the University of Houston, and the IEEE GRSS Fusion Committee for providing the Houston  ... 
arXiv:2003.02822v2 fatcat:2l37q46y6ndqjooo6pkcqezmzi

Source Separation in Chemical Analysis : Recent achievements and perspectives

Leonardo Tomazeli Duarte, Said Moussaoui, Christian Jutten
2014 IEEE Signal Processing Magazine  
novel BSS paradigms.  ...  Interestingly, this scenario is close to that of found in speech separation and can be interpreted as a sparsity-based approach.  ... 
doi:10.1109/msp.2013.2296099 fatcat:ypfhyvxyirh2pl4kfi6hnte7g4

Image splicing detection with local illumination estimation

Yu Fan, Philippe Carre, Christine Fernandez-Maloigne
2015 2015 IEEE International Conference on Image Processing (ICIP)  
-Technical Program COI-P1.4 -MULTILAYER MANIFOLD AND SPARSITY CONSTRAINTED NONNEGATIVE MATRIXFACTORIZATION FOR HYPERSPECTRAL UNMIXING Shu ZHENQIU, Nanjing University of Science and Technology Zhou JUN  ...  Create novel network-based multimedia analytics, and build innovative cloud, wireless, and software-defined networking systems for video and collaboration .  ...  A decision-level fusion is then performed . The demonstration consists of a subject-specific training for say three hand actions followed by a real-time testing or operation .  ... 
doi:10.1109/icip.2015.7351341 dblp:conf/icip/FanCF15 fatcat:7ja5gjnp5rafvedc2nman7xcru

Proceedings of the third "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'16) [article]

V. Abrol, O. Absil, P.-A. Absil, S. Anthoine, P. Antoine, T. Arildsen, N. Bertin, F. Bleichrodt, J. Bobin, A. Bol, A. Bonnefoy, F. Caltagirone, V. Cambareri (+47 others)
2016 arXiv   pre-print
learning and inference; "Blind" inverse problems and dictionary learning; Optimization for sparse modelling; Information theory, geometry and randomness; Sparsity?  ...  and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing (e.g., optics, computer vision, genomics, biomedical, digital communication, channel estimation, astronomy);  ...  Acknowledgements This work was supported by the Romanian National Authority for Scientific Research, CNCS -UEFISCDI, project number PN-II-RU-TE-2014-4-2713. Acknowledgements We thank A.  ... 
arXiv:1609.04167v1 fatcat:cral5owpqremninl43bksxvenu

Deep Feature Learning for Spectral-Spatial Classification of Hyperspectral Remote Sensing Images

Fahim Irfan Alam, University, My, Jun Zhou
2019
This thesis aims at presenting novel strategies for the analysis and classifi cation of hyperspectral remote sensing images, placing the focal point on the investigation on deep networks for the extraction  ...  Therefore, hyperspectral imaging emerged as a well-suited technology for accurate image classi fication in remote sensing.  ...  Activation functions such as ReLU and a normalization layer can help in effectively applying sparsity and non-linearity for deep model-based unmixing model.  ... 
doi:10.25904/1912/1943 fatcat:6jewidsvwjcjpf6s5yi77cbofm

Crystal Cartography: Mapping Nanostructure with Scanning Electron Diffraction

Duncan Neil Johnstone, Apollo-University Of Cambridge Repository, Apollo-University Of Cambridge Repository, Paul Anthony Midgley
2019
This nanostructure bridges the gap between idealised crys- talline structure and real materials, playing a deterministic role in tailoring physico-chemical properties, as well as providing a basis for  ...  In this work, methods for the acquisition and analysis of 4D-SED data are developed and applied to reveal nanostructure in two and three-dimensions.  ...  A novel method for two-dimensional strain mapping based on optimization of the image transform has been demonstrated, which has the methodological advantage of recasting the strain mapping problem as a  ... 
doi:10.17863/cam.42276 fatcat:matadmwemfeqpd6k3wajjxswse