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Hyperspectral Image Mixed Noise Removal Using a Subspace Projection Attention and Residual Channel Attention Network

Hezhi Sun, Ke Zheng, Ming Liu, Chao Li, Dong Yang, Jindong Li
2022 Remote Sensing  
To address the above issues, a novel HSI mixed noise removal network called subspace projection attention and residual channel attention network (SPARCA-Net) is proposed.  ...  Specifically, we propose an orthogonal subspace projection attention (OSPA) module to adaptively learn to generate bases of the signal subspace and project the input into such space to remove noise.  ...  Conclusions In this work, a novel HSI denoising network for mixed noise removal called subspace projection attention and channel attention network (SPARCA) was proposed, in which the proposed OSPA and  ... 
doi:10.3390/rs14092071 fatcat:jrsef72hq5eurdgear22yr4wei

Learnable Reconstruction Methods from RGB Images to Hyperspectral Imaging: A Survey [article]

Jingang Zhang and Runmu Su and Wenqi Ren and Qiang Fu and Yunfeng Nie
2021 arXiv   pre-print
However, the devices for acquiring hyperspectral images are expensive and complicated.  ...  Hyperspectral imaging enables versatile applications due to its competence in capturing abundant spatial and spectral information, which are crucial for identifying substances.  ...  ACKNOWLEDGMENT The authors would like to thank the authors of Sparse Coding, HSCNN, SR2D/3DNet, SRUNet, SRMSCNN, and etc. for providing open-source code.  ... 
arXiv:2106.15944v1 fatcat:zisfnjrs3nfkjexubp7f4esk4a

Unmixing based PAN guided fusion network for hyperspectral imagery [article]

Shuangliang Li, Yugang Tian, Hao Xia, Qingwei Liu
2022 arXiv   pre-print
The hyperspectral image (HSI) has been widely used in many applications due to its fruitful spectral information.  ...  Note that the fusion process of the proposed network is under the projected low-dimensional abundance subspace with an extremely large fusion ratio of 16.  ...  Differently, the CSC framework projects the input image into a highdimensional feature subspace while our designed network is in the low-dimensional subspace with less complexity.  ... 
arXiv:2201.11318v1 fatcat:evfsgefz5veobgetkb4au5z3x4

HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection [article]

Meiqi Hu, Chen Wu, Liangpei Zhang
2022 arXiv   pre-print
Instead of processing the two-dimensional imaging space and spectral response dimension in hybrid style, a powerful spatial-spectral attention module is put forward to explore the spatial correlation and  ...  In this paper, we proposed a novel pixel-level self-supervised hyperspectral spatial-spectral understanding network (HyperNet) to accomplish pixel-wise feature representation for effective hyperspectral  ...  Fig. 2 2 The architecture of (a) residual spatial attention block (RSAB); (b) residual channel attention block (RCAB); (c) adaptive feature fusion block; (d) channel-wise attention, and (e) spatial-wise  ... 
arXiv:2207.09634v1 fatcat:znzildxolfbztko6p5bwr5zqqq

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 143-153 Impulse noise Hyperspectral Mixed Noise Removal By , 1 -Norm-Based Subspace Representation.  ...  ., +, JSTARS 2020 859-871 Hyperspectral Mixed Noise Removal By , 1 -Norm-Based Subspace Representation.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 3374-3387 A Weighted Fidelity and Regularization-Based Method for Mixed or Unknown Noise Removal From Images on Graphs.  ...  ., +, TIP 2020 5531-5541 Gradient methods A Weighted Fidelity and Regularization-Based Method for Mixed or Unknown Noise Removal From Images on Graphs.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Hyperspectral Anomaly Detection based on Machine Learning: An Overview

Yichu Xu, Lefei Zhang, Bo Du, Liangpei Zhang
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Hyperspectral anomaly detection (HAD) is an important hyperspectral image application.  ...  Finally, conclusions regarding HAD are summarized, and prospects and future development direction are discussed.  ...  The image has 224 spectral channels in wavelengths ranging from 370 to 2510 nm. A total of 189 bands remained after removing the low SNR, water absorption, and bad bands.  ... 
doi:10.1109/jstars.2022.3167830 fatcat:zdhdwbglrnbjfdf5w5trsopizi

2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57

2019 IEEE Transactions on Geoscience and Remote Sensing  
and Hanssen, R.F., Incorporating Temporary Coherent Li, X., Yeo, T.S., Yang, Y., Chi, C., Zuo, F., Hu, X., and Pi, Y., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging  ...  Hu, C., Zhang, B., Dong, X., and Li, Y., Geosynchronous SAR Tomography: Theory and First Experimental Verification Using Beidou IGSO Satellite; TGRS Sept. 2019 6591-6607 Hu, F., Wu, J., Chang, L.,  ...  ., +, TGRS March 2019 1779-1792 Remote Sensing Image Superresolution Using Deep Residual Channel Attention.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

Table of Contents

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Wang, and Y. Zhang 1045 Hyperspectral Mixed Noise Removal By 1 -Norm-Based Subspace Representation . . . . . . . . . . . L. Zhuang and M. K.  ...  Gloaguen 4214 3-D Channel and Spatial Attention-Based Multiscale Spatial-Spectral Residual Network for Hyperspectral Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Wang 3862 Channel-Attention-Based DenseNet Network for Remote Sensing Image Scene Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jstars.2020.3046663 fatcat:zqzyhnzacjfdjeejvzokfy4qze

Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution [article]

Jing Yao, Danfeng Hong, Jocelyn Chanussot, Deyu Meng, Xiaoxiang Zhu, Zongben Xu
2020 arXiv   pre-print
Moreover, a cross-attention module is devised to yield more effective spatial-spectral information transfer in networks.  ...  The recent advancement of deep learning techniques has made great progress on hyperspectral image super-resolution (HSI-SR).  ...  According to a subspace assumption, Bayesian approach was first introduced by Eismann et al. utilizing a stochastic mixing model [8] , and developed through subsequent researches by exploiting more inherent  ... 
arXiv:2007.05230v3 fatcat:5sdnosszwfdf5jfgduwyylfua4

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  
Hence, rigorous and innovative methodologies are required for hyperspectral image and signal processing and have become a center of attention for researchers worldwide.  ...  However, a huge number of factors, such as high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light scattering mechanisms in the acquisition process, and different  ...  In addition, the authors would like to thank the National Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the CASI Houston data set, and the IEEE GRSS Image Analysis  ... 
doi:10.1109/mgrs.2017.2762087 fatcat:6ezzye7yyvacbouduqv2f2c7gi

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, TIP 2021 6168-6183 Fast Roughness Minimizing Image Restoration Under Mixed Poisson-Gaussian Noise.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

2021 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 14

2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages.  ...  ., +, JSTARS 2021 1628-1644 Hyperspectral Mixed Noise Removal via Spatial-Spectral Constrained Unsupervised Deep Image Prior.  ... 
doi:10.1109/jstars.2022.3143012 fatcat:dnetkulbyvdyne7zxlblmek2qy

Multiscale Information Fusion for Hyperspectral Image Classification Based on Hybrid 2D-3D CNN

Hang Gong, Qiuxia Li, Chunlai Li, Haishan Dai, Zhiping He, Wenjing Wang, Haoyang Li, Feng Han, Abudusalamu Tuniyazi, Tingkui Mu
2021 Remote Sensing  
To process the high dimensionality and high nonlinearity of hyperspectral images, deep learning methods based on convolutional neural network (CNN) are widely used in hyperspectral classification applications  ...  When such networks meet the practical challenge of a limited labeled hyperspectral image dataset—i.e., "small sample problem"—the classification accuracy and generalization ability would be limited.  ...  Veganzons, and B. Ayerdi for collecting the hyperspectral datasets and free downloads from http://www.ehu.eus/ccwintco/ indexphp/Hyperspectral_Remote_Sensing_Scenes (accessed on 21 June 2019).  ... 
doi:10.3390/rs13122268 fatcat:23izxydojnd7ddbdhaq3ragb5u

Table of contents

2020 IEEE Transactions on Image Processing  
Xing, and Z. Li 3927 A Weighted Fidelity and Regularization-Based Method for Mixed or Unknown Noise Removal From Images on Graphs ...................................................................  ...  Wang 8760 Image Interpolation Using Multi-Scale Attention-Aware Inception Network ........... J. Ji, B. Zhong, and K.-K.  ...  Lin, and Zhang, Y. Tian, K. Wang, W. Zhang, and F.-  ... 
doi:10.1109/tip.2019.2940373 fatcat:i7hktzn4wrfz5dhq7hj75u6esa
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