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Hyperspectral Image Super-Resolution Using Optimization and DCNN-Based Methods
[chapter]
2019
Processing and Analysis of Hyperspectral Data [Working Title]
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The optimization-based approaches estimate HR-HS image via minimizing the reconstruction errors of the available low-resolution hyperspectral and high-resolution multispectral images with different constrained ...
This chapter provides a comprehensive description of not only the conventional optimization-based methods but also the recently investigated DCNN-based learning methods for HS image super-resolution, which ...
Self-similarity constrained sparse representation for HS image super-resolution The complete pipeline of self-constrained sparse representation for HS image super-resolution is illustrated in Figure 2 ...
doi:10.5772/intechopen.89243
fatcat:rxalxrj3yre2zccqwfpqbxqn4a
Adaptive non-negative sparse representation for hyperspectral image super-resolution
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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As the Hyperspectral (HS) images usually have low spatial resolution, hyperspectral image (HSI) super-resolution has recently attracted more and more attention to enhance the spatial resolution of HSIs ...
Experimental results on both ground-based hyperspectral images and real remote sensing HSIs show the superiority of our proposed approach to some other state-of-the-art HSI super-resolution methods. ...
representation for hyperspectral image super-resolution Xuesong Li, Youqiang Zhang, Zixian Ge, Guo Cao, Hao Shi, and Peng Fu Target high spatial resolution hyperspectral image Low spatial resolution hyperspectral ...
doi:10.1109/jstars.2021.3072044
fatcat:3emcdm6kbjbbzl4rhhvznb25e4
Sparse Representation Classification Based on Flexible Patches Sampling of Superpixels for Hyperspectral Images
2018
Mathematical Problems in Engineering
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Aiming at solving the difficulty of modeling on spatial coherence, complete feature extraction, and sparse representation in hyperspectral image classification, a joint sparse representation classification ...
Then, we design a joint sparse recovery model by sampling overcomplete patches of superpixels to estimate joint sparse characteristics of test pixel, which are carried out on the orthogonal matching pursuit ...
Sparse representation technology has been applied in various fields of computer vision pattern recognition, such as image segmentation, image restoration, super resolution, and face recognition. ...
doi:10.1155/2018/8264961
fatcat:jgbe2i2xf5au7b6tib3nuiosve
Table of contents
2019
IEEE Transactions on Geoscience and Remote Sensing
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4967 PAN-Guided Cross-Resolution Projection for Local Adaptive Sparse Representation-Based Pansharpening ........... .................................................................................... ...
Tang 4457 Superpixel Tensor Model for Spatial-Spectral Classification of Remote Sensing Images ..... Y. Gu, T. Liu, and J. ...
doi:10.1109/tgrs.2019.2923179
fatcat:nfaahnqzcvft5ezz36a6nyy2ri
Table of contents
2019
IEEE Transactions on Image Processing
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Visvikis 3075 Interpolation, Super-Resolution, and Mosaicing Nonlocal Patch Tensor Sparse Representation for Hyperspectral Image Super-Resolution ................................. ..................... ...
Flierl 2731 Radar Imaging, Remote Sensing, and Geophysical Imaging Nonconvex-Sparsity and Nonlocal-Smoothness-Based Blind Hyperspectral Unmixing ..................................... .................. ...
doi:10.1109/tip.2019.2905937
fatcat:hdjjnnguk5dera2si44df5zude
Table of contents
2020
IEEE Transactions on Geoscience and Remote Sensing
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Li 5998 CNN-Based Super-Resolution of Hyperspectral Images ......................................................................... ................................................................ ...
Sparse Super-Resolution Method for Radar Forward-Looking Imaging .................................................. ............................................... ...
doi:10.1109/tgrs.2020.3006605
fatcat:g45mqghjmjenlfd2ydw7nzbcju
2020 Index IEEE Transactions on Computational Imaging Vol. 6
2020
IEEE Transactions on Computational Imaging
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., +, TCI 2020 248-262
Multimodal Image Super-Resolution via Joint Sparse Representations
Induced by Coupled Dictionaries. ...
., +, TCI 2020 248-262
Multimodal Image Super-Resolution via Joint Sparse Representations
Induced by Coupled Dictionaries. ...
., +, TCI 2020 125-137 Truncation Correction for X-ray Phase-Contrast Region-of-Interest Tomography. Felsner, L., +, TCI 2020 625-639 ...
doi:10.1109/tci.2021.3054596
fatcat:puij7ztll5ai7alxrmqzsupcny
Table of contents
2019
IEEE Transactions on Image Processing
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Wu 3020 Nonlocal Patch Tensor Sparse Representation for Hyperspectral Image Super-Resolution ................................. .......................................................................... ...
Li 2882 On the Diversity of Conditional Image Synthesis With Semantic Layouts ................ Z. Yang, H. Liu, and D. ...
doi:10.1109/tip.2019.2905936
fatcat:xzdsx3tjbjfohhebapnp6gtb4u
Table of contents
2021
IEEE Transactions on Geoscience and Remote Sensing
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Sveinsson 535 Hyperspectral and Multispectral Image Fusion via Nonlocal Low-Rank Tensor Approximation and Sparse Representation ......................................................................... ...
Chanussot, and D.
613 Superpixel-Based Reweighted Low-Rank and Total Variation Sparse Unmixing for Hyperspectral Remote Sensing Imagery ............................................................... ...
doi:10.1109/tgrs.2020.3039449
fatcat:sxee5msl55emhnszejgpf6vrpa
Hyperspectral Anomaly Detection Based on Low-Rank Representation with Data-Driven Projection and Dictionary Construction
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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In order to improve the separability of the background and anomaly, we propose a novel hyperspectral anomaly detection based on low-rank representation with dictionary construction and data-driven projection ...
To construct a robust dictionary that contains all categories of the background objects whilst excluding the anomaly's influence, we adopt a superpixel-based tensor low-rank decomposition method to generate ...
developed the nonlocal patch tensor-based sparse representation for HSI super-resolution [19] . Nowadays, the sparse representation with low-rank constraint models has been explored. ...
doi:10.1109/jstars.2020.2990457
fatcat:lpp4zovkyjgnzndf4lex4uc7ge
Table of Contents
2020
IEEE Transactions on Computational Imaging
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and Low Rank Models Multimodal Image Super-Resolution via Joint Sparse Representations Induced by Coupled Dictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Rahardja 1070 Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery . . . . . J. Jiang, H. Sun, X. Liu, and J. ...
doi:10.1109/tci.2021.3054280
fatcat:7se3scatcrcutgat3tpk5mz2nm
Hyperspectral Image Classification via Joint Sparse representation of Multi-layer Superpixles
2018
KSII Transactions on Internet and Information Systems
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In this paper, a novel spectral-spatial joint sparse representation algorithm for hyperspectral image classification is proposed based on multi-layer superpixels in various scales. ...
Experimental results on three real hyperspectral datasets show that the proposed joint sparse model can achieve better performance than a series of excellent sparse classification methods and superpixels-based ...
Related Work The research in this paper is based on the superpixels extraction, sparse representation classification and joint sparse representation. ...
doi:10.3837/tiis.2018.10.021
fatcat:3d7qwd2rifdw5fu3w45lr7tlkq
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., +, TIP 2020 3638-3651 A Truncated Matrix Decomposition for Hyperspectral Image Super-Resolution. ...
Xompero, A., +, TIP 2020 4362-4375 A Truncated Matrix Decomposition for Hyperspectral Image Super-Resolution. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
2015 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 53
2015
IEEE Transactions on Geoscience and Remote Sensing
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., +, TGRS July 2015 3658-3668 Improving the Spatial Resolution of Landsat TM/ETM+ Through Fusion With SPOT5 Images via Learning-Based Super-Resolution. ...
., +,
TGRS Feb. 2015 631-644
Improving the Spatial Resolution of Landsat TM/ETM+ Through Fusion
With SPOT5 Images via Learning-Based Super-Resolution. ...
doi:10.1109/tgrs.2015.2513444
fatcat:zuklkpk4gjdxjegoym5oagotzq
A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Recent developments in hyperspectral sensors have made it possible to acquire HyperSpectral Images (HSI) with higher spectral and spatial resolution. ...
Superpixel segmentation is a process of segmenting the spatial image into several semantic sub-regions with similar characteristic features. ...
Sparse-representation-based methods [81] , Collaborative representation based methods [49] and Low-rank representation based methods [66] , [51] are some of the popular sparsity based methods used ...
doi:10.1109/jstars.2021.3076005
fatcat:smfb6jeox5eldbv6ys7ioeoko4
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