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Advances in Deep Learning for Hyperspectral Image Analysis—Addressing Challenges Arising in Practical Imaging Scenarios [chapter]

Xiong Zhou, Saurabh Prasad
2020 Advances in Computer Vision and Pattern Recognition  
In recent years, there has been an emergence of deep learning algorithms being applied to hyperspectral and multispectral imagery for remote sensing and biomedicine tasks.  ...  In this chapter, we will review recent advances in the community that leverage deep learning for robust hyperspectral image analysis despite these unique challenges -- specifically, we will review unsupervised  ...  In order to undertake domain adaptation, features from the two domains were aligned by minimizing the distribution difference.  ... 
doi:10.1007/978-3-030-38617-7_5 fatcat:23ibk4ojbvepbpikxgjxan4i6e

Adversarial Learning based Discriminative Domain Adaptation for Geospatial Image Analysis

Nikhil Makkar, Hsiuhan Lexie Yang, Saurabh Prasad
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
We are using adversarial learning to extract discriminative target domain features that are aligned with source domain.  ...  We test our framework for two very different applications of remote sensing imagery, multiclass classification in hyperspectral images and semantic segmentation in large scale satellite images.  ...  Recent research in domain adaptation for hyperspectral image classification focuses on a mix of deep learning and classical machine learning. [35] shows a tensor alignment domain adaptation strategy based  ... 
doi:10.1109/jstars.2021.3132259 fatcat:5ppi25cwirc2bmnlgolauiwga4

Augmented Associative Learning Based Domain Adaptation for Classification of Hyperspectral Remote Sensing Images

Min Chen, Li Ma, Wenjin Wang, Qian Du
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Associative learning based domain adaptation approach is investigated for classification of hyperspectral remote sensing images in this paper.  ...  The AAL based domain adaptation network does not require target labeled information and can achieve unsupervised classification of the target image.  ...  ACKNOWLEDGEMENT The authors would like to thank Professor Melba Crawford at Purdue University for providing the BOT and KSC data used in this study.  ... 
doi:10.1109/jstars.2020.3030304 fatcat:tyqejjf2pbfcbd4azt5zvgyyt4

Heterogeneous Spectral-Spatial Feature Transfer with Structure Preserved Distribution Alignment for Hyperspectral Image Classification

Chongxiao Zhong, Junping Zhang, Qingle Guo, Ye Zhang
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Then, in order to overcome the heterogeneity between the two feature sets, we build a structure preserved distribution alignment (SPDA) model to learn domain-specific projections to map the feature samples  ...  By performing MCSD, the spectral information and spatial structure information at different scales can be jointly adapted to learn transferable features for classification.  ...  In [20] , Li et al. first proposed a transfer learning framework for heterogeneous remote sens-Heterogeneous Spectral-Spatial Feature Transfer with Structure Preserved Distribution Alignment for Hyperspectral  ... 
doi:10.1109/jstars.2022.3187757 fatcat:y4okvsqhsvfx3g46clf2st4kp4

Joint Correlation Alignment-Based Graph Neural Network for Domain Adaptation of Multitemporal Hyperspectral Remote Sensing Images

Wenjin Wang, Li Ma, Min Chen, Qian Du
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In this article, we propose a novel deep domain adaptation method based on graph neural network (GNN) for multitemporal hyperspectral remote sensing images.  ...  Furthermore, the domain-wise correlation alignment (CORAL) and class-wise CORAL are jointly embedded in GNN network to achieve a joint distribution adaptation performance.  ...  ACKNOWLEDGMENT The authors would like to thank Professor Melba Crawford at Purdue University for providing the BOT data and thank the Hyperspectral Image Analysis Group and the National Center for Airborne  ... 
doi:10.1109/jstars.2021.3063460 fatcat:dmxlnunrx5bpbfjcejlbygwbt4

Graph Embedding and Distribution Alignment for Domain Adaptation in Hyperspectral Image Classification

Yi Huang, Jiangtao Peng, Yujie Ning, Weiwei Sun, Qian Du
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In this paper, we propose a new domain adaptation (DA) method for hyperspectral image (HSI) classification, called graph embedding and distribution alignment (GEDA).  ...  Recent studies in cross-domain classification have shown that discriminant information of both source and target domains is very important.  ...  From the above traditional and deep-learning-based DA methods, we can see that feature learning and distribution alignment are key factors for domain adaptation.  ... 
doi:10.1109/jstars.2021.3099805 fatcat:mda5ixj26bab5oxb4ykemo266y

CoSpace: Common Subspace Learning From Hyperspectral-Multispectral Correspondences

Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu
2019 IEEE Transactions on Geoscience and Remote Sensing  
Hyperspectral imaging enables discrimination between spectrally similar classes but its swath width from space is narrow compared to multispectral ones.  ...  The multispectral out-of-samples can be then projected into the subspace, which are expected to take advantages of rich spectral information of the corresponding hyperspectral data used for learning, and  ...  ACKNOWLEDGMENT The authors would like to thank the Hyperspectral Image Analysis Group and the NSF Funded Center for Airborne Laser Mapping at the University of Houston for providing the CASI University  ... 
doi:10.1109/tgrs.2018.2890705 fatcat:rpyapnl5rrfmzfpiozitjyjbze

Ideal Regularized Discriminative Multiple Kernel Subspace Alignment for Domain Adaptation in Hyperspectral Image Classification

Weidong Yang, Jiangtao Peng, Weiwei Sun
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This paper proposes a novel unsupervised domain adaption (DA) method called ideal regularized discriminative multiple kernel subspace alignment (IRDMKSA) for hyperspectral image (HSI) classification.  ...  Experimental results on four domain adaptation tasks show that the performance of IRDMKSA is better than some classical unsupervised DA methods for the HSI classification.  ...  For more information, see https://creativecommons.org/licenses/by/4.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited.  ... 
doi:10.1109/jstars.2020.3026316 fatcat:aokwar5w6nhnfntus7lbfuphty

Tensor Representation and Manifold Learning Methods for Remote Sensing Images [article]

Lefei Zhang
2014 arXiv   pre-print
This thesis targets to develop some efficient information extraction algorithms for RS images, by relying on the advanced technologies in machine learning.  ...  More precisely, we adopt the manifold learning algorithms as the mainline and unify the regularization theory, tensor-based method, sparse learning and transfer learning into the same framework.  ...  feature, in both the spatial and spectral domains, to improve the classification accuracy.  ... 
arXiv:1401.2871v1 fatcat:7riwgc3pc5hcpm3iczsy2tsali

Sample Generation with Self-Attention Generative Adversarial Adaptation Network (SaGAAN) for Hyperspectral Image Classification

Wenzhi Zhao, Xi Chen, Jiage Chen, Yang Qu
2020 Remote Sensing  
Hyperspectral image analysis plays an important role in agriculture, mineral industry, and for military purposes.  ...  To generate high-quality hyperspectral samples, a self-attention generative adversarial adaptation network (SaGAAN) is proposed in this work.  ...  In the future, we still need to focus on the spatial feature generation, which is also important for hyperspectral image classification.  ... 
doi:10.3390/rs12050843 fatcat:qiublsnjbrfjxl2ibjqyykmtke

Adaptive Local Discriminant Analysis and Distribution Matching for Domain Adaptation in Hyperspectral Image Classification

Yujie Ning, Jiangtao Peng, Lin Sun, Yi Huang, Weiwei Sun, Qian Du
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In this article, an adaptive local discriminant analysis and distribution matching (ALDADM) method is designed for the domain adaptation (DA) in HSI classification.  ...  Multimodally distributed data is very common in remote sensing images, such as hyperspectral images (HSIs).  ...  Gamba for providing the University of Pavia and Center of Pavia datasets.  ... 
doi:10.1109/jstars.2022.3181577 fatcat:ae3p4lk2afa4bgm7nvkzjsqfom

Graph-Embedding Balanced Transfer Subspace Learning for Hyperspectral Cross-Scene Classification

Yongsheng Zhou, Peiyun Chen, Na Liu, Qiang Yin, Fan Zhang
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Hyperspectral cross-scene classification utilizes the prior knowledge of source scenes with known labels to classify unlabeled target scenes via transfer learning.  ...  The existing methods did not properly balance the contribution of marginal and conditional distribution to transfer learning.  ...  , is vital for hyperspectral image classification.  ... 
doi:10.1109/jstars.2022.3163423 fatcat:55ejkkjwizbdbls6laqgsoit4y

Table of contents

2020 IEEE Geoscience and Remote Sensing Letters  
Wu, and X. Hu 1440 Discriminative Eigenpixels-Based Dictionary Learning for Hyperspectral Image Classification .... L. Song and S.  ...  Li 1363 Hyperspectral Data Combining t-Distributed Stochastic Neighbor Embedding With Convolutional Neural Networks for Hyperspectral Image Classification ........................................ L.  ... 
doi:10.1109/lgrs.2020.3006659 fatcat:ewiznjupqvcfzkoj6atbtr5nae

Hyperspectral Image Classification Based on Unsupervised Heterogeneous Domain Adaptation CycleGan

Xuesong Wang, Yiran Li, Yuhu Cheng
2020 Chinese journal of electronics  
Aiming at the difficulty of obtaining sufficient labeled Hyperspectral image (HSI) data and the inconsistent feature distribution of different HSIs, a novel Unsupervised heterogeneous domain adaptation  ...  , realizing the alignment of feature distributions.  ...  Introduction Hyperspectral image (HSI) classification has always been a hot issue in the field of hyperspectral remote sensing, with the purpose of dividing each pixel into different labels according to  ... 
doi:10.1049/cje.2020.05.003 fatcat:uoumlfwvozbbvputu6mnjxgnuq

Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification

Danfeng Hong, Naoto Yokoya, Nan Ge, Jocelyn Chanussot, Xiao Xiang Zhu
2019 ISPRS journal of photogrammetry and remote sensing (Print)  
In this paper, we aim at tackling a general but interesting cross-modality feature learning question in remote sensing community-can a limited amount of highly-discriminative (e.g., hyperspectral) training  ...  Traditional semi-supervised manifold alignment methods do not perform sufficiently well for such problems, since the hyperspectral data is very expensive to be largely collected in a trade-off between  ...  Cai and Dr. C. Wang for providing MATLAB codes for LPP and manifold alignment algorithms.  ... 
doi:10.1016/j.isprsjprs.2018.10.006 pmid:30774220 pmcid:PMC6360532 fatcat:upiqn5ouczdazk5z4inzrguqxu
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