Filters








120 Hits in 5.1 sec

Coupled Convolutional Neural Network With Adaptive Response Function Learning for Unsupervised Hyperspectral Super Resolution

Ke Zheng, Lianru Gao, Wenzhi Liao, Danfeng Hong, Bing Zhang, Ximin Cui, Jocelyn Chanussot
2020 IEEE Transactions on Geoscience and Remote Sensing  
Hyperspectral super-resolution refers to fusing HSI and MSI to generate an image with both high spatial and high spectral resolutions.  ...  Two special convolutional layers are designed to act as a bridge that coordinates with the three autoencoder nets, and the PSF and SRF parameters are learned adaptively in the two convolution layers during  ...  Naoto Yokoya for providing MATLAB codes for hyperspectral and multispectral data fusion toolbox  ... 
doi:10.1109/tgrs.2020.3006534 fatcat:u24k5a5vpncirhbourj5yzb2fu

Table of contents

2021 IEEE Transactions on Geoscience and Remote Sensing  
Figueiredo 2478 Coupled Convolutional Neural Network With Adaptive Response Function Learning for Unsupervised Hyperspectral Super Resolution ......................... K. Zheng, L. Gao, W. Liao, D.  ...  Zhang 2307 Hybrid 2-D-3-D Deep Residual Attentional Network With Structure Tensor Constraints for Spectral Super-Resolution of RGB Images ................................................... J. Li, C.  ... 
doi:10.1109/tgrs.2021.3052119 fatcat:obk5h6sp2nh47ounq4jqlhukcu

Deep Unsupervised Fusion Learning for Hyperspectral Image Super Resolution

Zhe Liu, Yinqiang Zheng, Xian-Hua Han
2021 Sensors  
for HS image super-resolution.  ...  Recently, deep-learning-based methods evolved for automatically learning the abundant image priors in a latent HR-HS image. These methods have made great progress for HS image super resolution.  ...  Conclusions In this work, a deep unsupervised fusion-learning framework for the hyperspectral image super-resolution problem was proposed.  ... 
doi:10.3390/s21072348 pmid:33800532 fatcat:a5iqebvpjbbtlpvnhszihlh2ie

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

2019 IEEE Transactions on Geoscience and Remote Sensing  
., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging on Arbitrary Region of Interest; TGRS Oct. 2019 7995-8010 Hu, T., see Kang, Z., TGRS Jan. 2019 181-193 Hu, T.,  ...  ., +, TGRS Aug. 2019 6169-6181 Spectral Super Resolution of Hyperspectral Images via Coupled Dictionary Learning.  ...  ., +, TGRS July 2019 5085-5097 Spectral Super Resolution of Hyperspectral Images via Coupled Dictionary Learning.  ... 
doi:10.1109/tgrs.2020.2967201 fatcat:kpfxoidv5bgcfo36zfsnxe4aj4

Hyperspectral Image Super-Resolution With Optimized RGB Guidance

Ying Fu, Tao Zhang, Yinqiang Zheng, Debing Zhang, Hua Huang
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
In this paper, we first present a simple and efficient convolutional neural network (CNN) based method for HSI superresolution in an unsupervised way, without any prior training.  ...  To overcome the limitations of existing hyperspectral cameras on spatial/temporal resolution, fusing a low resolution hyperspectral image (HSI) with a high resolution RGB (or multispectral) image into  ...  Beyond CSR selection, we simulate the CSR as a convolution layer to learn the optimal CSR for RGBguided HSI super-resolution.  ... 
doi:10.1109/cvpr.2019.01193 dblp:conf/cvpr/FuZZZ019 fatcat:zbmb5ssbuvcnta355zh45n5rwu

2021 Index IEEE Transactions on Image Processing Vol. 30

2021 IEEE Transactions on Image Processing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Direct Unsupervised Super-Resolution Using Generative Adversarial Network (DUS-GAN) for Real-World Data.  ...  ., +, TIP 2021 6226-6239 Deep Coupled Feedback Network for Joint Exposure Fusion and Image Super-Resolution.  ... 
doi:10.1109/tip.2022.3142569 fatcat:z26yhwuecbgrnb2czhwjlf73qu

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 5776-5788 Hyperspectral Image Super Resolution Based on Multiscale Feature Fusion and Aggregation Network With 3-D Convolution.  ...  ., +, JSTARS 2020 3367-3380 Hyperspectral Image Super Resolution Based on Multiscale Feature Fusion and Aggregation Network With 3-D Convolution.  ...  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

UMAG-Net: A New Unsupervised Multiattention-Guided Network for Hyperspectral and Multispectral Image Fusion

Shuaiqi Liu, Siyu Miao, Jian Su, Bing Li, Weiming Hu, Yu-Dong Zhang
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
To reconstruct images with high spatial resolution and high spectral resolution, one of the most common methods is to fuse a low-resolution hyperspectral image (HSI) with a high-resolution (HR) multispectral  ...  Index Terms-Deep learning, hyperspectral images (HSIs), image fusion, multispectral images (MSIs).  ...  For fusing LR-HSI and HR-MSI, we proposed an unsupervised multiattentive guidance network to get better HSI super-resolution reconstruction.  ... 
doi:10.1109/jstars.2021.3097178 fatcat:kxoiq4fyc5c4bjhhjceig4xdpq

Table of Contents

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
-I Chang 2485 Adaptive Residual Convolutional Neural Network for Hyperspectral Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Karimi 3066 Super-Resolution for MIMO Array SAR 3-D Imaging Based on Compressive Sensing and Deep Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Nian 4352 Hyperspectral Image Super Resolution Based on Multiscale Feature Fusion and Aggregation Network With 3-D Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jstars.2020.3046663 fatcat:zqzyhnzacjfdjeejvzokfy4qze

Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

Alberto Signoroni, Mattia Savardi, Annalisa Baronio, Sergio Benini
2019 Journal of Imaging  
The present review develops on two fronts: on the one hand, it is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning  ...  Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain.  ...  In [196] , Mou et al. proposed, for the first time in HSI, an end-to-end 2-D fully Convolution-Deconvolution network for unsupervised spectral-spatial feature learning.  ... 
doi:10.3390/jimaging5050052 pmid:34460490 fatcat:ledlmt42bfdtdhe7tvj2dl2rwm

Introduction to the Special Issue on Tensor Decomposition for Signal Processing and Machine Learning

Hongyang Chen, Sergiy A. Vorobyov, Hing Cheung So, Fauzia Ahmad, Fatih Porikli
2021 IEEE Journal on Selected Topics in Signal Processing  
super-resolution as a coupled LL1 tensor decomposition problem.  ...  Sun et al. devise two coupled or joint tensor decomposition algorithms for deep convolutional neural network compression by exploring the joint information implied in the network structure.  ... 
doi:10.1109/jstsp.2021.3065184 fatcat:qbvihejwkfaa5hoztety77pnwi

Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual Learning [article]

Zhiyu Zhu, Junhui Hou, Jie Chen, Huanqiang Zeng, Jiantao Zhou
2020 arXiv   pre-print
This paper explores the problem of hyperspectral image (HSI) super-resolution that merges a low resolution HSI (LR-HSI) and a high resolution multispectral image (HR-MSI).  ...  To learn the residual image efficiently and effectively, we employ spectral-spatial separable convolution with dense connections.  ...  [41] proposed an unsupervised coupled CNN with an adaptive response function for HSI super-resolution. Arun et al.  ... 
arXiv:2006.10300v2 fatcat:cehetwqw6fhhbisxzso632awwq

Deep Learning for Land Use and Land Cover Classification based on Hyperspectral and Multispectral Earth Observation Data: A Review

Ava Vali, Sara Comai, Matteo Matteucci
2020 Remote Sensing  
Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for  ...  Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data.  ...  extracted features), and concluded by a fully connected neural network and an activation function.  ... 
doi:10.3390/rs12152495 fatcat:2zcqsuejjrcplmj4dycgsyen7m

HPRN: Holistic Prior-embedded Relation Network for Spectral Super-Resolution [article]

Chaoxiong Wu, Jiaojiao Li, Rui Song, Yunsong Li, Qian Du
2021 arXiv   pre-print
Spectral super-resolution (SSR) refers to the hyperspectral image (HSI) recovery from an RGB counterpart.  ...  However, most current approaches only consider the general and limited priors in their designing the customized convolutional neural networks (CNNs), which leads to the inability to effectively alleviate  ...  Rahardja, resolution using convolutional neural network,” IEEE Transactions on “Reconstruction of hyperspectral data from rgb images with prior Geoscience and Remote Sensing, vol.  ... 
arXiv:2112.14608v1 fatcat:bqbfdba2afflrd5our5s3g6tqe

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  
Index Terms-Adaptive sparse representation, hyperspectral image, spectral basis updating, super-resolution reconstruction.  ...  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  ...  [39] presented a convolution neural network with two branches.  ... 
doi:10.1109/jstars.2021.3072044 fatcat:3emcdm6kbjbbzl4rhhvznb25e4
« Previous Showing results 1 — 15 out of 120 results