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Hyperspectral Image Super-Resolution Using Optimization and DCNN-Based Methods [chapter]

Xian-Hua Han
2019 Processing and Analysis of Hyperspectral Data [Working Title]  
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

Xuesong Li, Youqiang Zhang, Zixian Ge, Hao Shi, Guo Cao, Peng Fu
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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

Haifeng Sima, Pei Liu, Lanlan Liu, Aizhong Mi, Jianfang Wang
2018 Mathematical Problems in Engineering  
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  
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  
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  
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  
., +, 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  
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  
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

Xiaoxiao Ma, Xiangrong Zhang, Xu Tang, Huiyu Zhou, Licheng JIAO
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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  
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  
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  
., +, 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  
., +, 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

Subhashree Subudhi, Ram Narayan Patro, Pradyut Kumar Biswal, Fabio Dell'Acqua
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
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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