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Semisupervised Discriminative Locally Enhanced Alignment for Hyperspectral Image Classification
2013
IEEE Transactions on Geoscience and Remote Sensing
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Experiments with extensive hyperspectral image data sets showed that the proposed algorithm is notably superior to other state-of-the-art dimensionality reduction methods for hyperspectral image classification ...
This paper proposes a new semisupervised dimension reduction (DR) algorithm based on a discriminative locally enhanced alignment technique. ...
Gamba from the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society for providing the ROSIS data set and the handling editor and anonymous reviewers for their careful reading ...
doi:10.1109/tgrs.2012.2230445
fatcat:w437tg7jhngp7fm2pzup7bl6ue
Hyperspectral Image Classification with Spectral and Spatial Graph using Inductive Representation Learning Network
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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It constructs the graph by sampling and aggregating (GraphSAGE) feature from a node's local neighborhood. This could greatly reduce the space complexity. ...
Convolutional neural networks (CNN) have achieved excellent performance for the hyperspectral image (HSI) classification problem due to better extracting spectral and spatial information. ...
[38] also proposed a graph-based learning method for hyperspectral image classification using superpixels. ...
doi:10.1109/jstars.2020.3042959
fatcat:ap3nl4cu2bcb7oyf6g6z7f33qm
Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification
2020
IEEE Transactions on Geoscience and Remote Sensing
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In this article, we propose a novel graph-based semisupervised network called nonlocal graph convolutional network (nonlocal GCN). ...
Hence it would be conceptually of great interest to explore networks that are able to exploit labeled and unlabeled data simultaneously for hyperspectral image classification. ...
The contributions of this article are threefold. 1) We perform hyperspectral image classification via a graph-based semisupervised network. ...
doi:10.1109/tgrs.2020.2973363
fatcat:2xt4zpifnbbzbhmpb5xcxpanoy
Hyperspectral Image Classification Through Bilayer Graph-Based Learning
2014
IEEE Transactions on Image Processing
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Hyperspectral image classification with limited number of labeled pixels is a challenging task. In this paper, we propose a bilayer graph-based learning framework to address this problem. ...
For graph-based classification, how to establish the neighboring relationship among the pixels from the high dimensional features is the key toward a successful classification. ...
In semisupervised graph based method, the hyperspectral image classification is formulated as a graph based semisupervised learning problem. ...
doi:10.1109/tip.2014.2319735
pmid:24771580
fatcat:6knm6gexiffl7drrh2b7xjzmey
Semi-supervised hypergraph discriminant learning for hyperspectral image classification
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Index Terms-Dimensionality reduction (DR), graph learning, hyperspectral image (HSI) classification, locality-constrained linear coding, neighborhood margin. ...
However, traditional semisupervised learning methods fail to consider multiple properties of an HSI, which has restricted the discriminant performance of feature representation. ...
ACKNOWLEDGMENT The authors would like to thank the handling editor and the anonymous reviewers for their detailed and constructive comments and suggestions, which indeed helped to improve the quality of ...
doi:10.1109/jstars.2020.3011431
fatcat:qa3iau3u2zeshnvewv47ddm5vi
Semisupervised Classification of Hyperspectral Image Based on Graph Convolutional Broad Network
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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To face this challenge, we propose a semisupervised HSI classification method based on graph convolutional broad network (GCBN). ...
Index Terms-Broad learning, classification, hyperspectral image (HSI), sample expansion, semisupervised learning. ...
SEMISUPERVISED CLASSIFICATION OF HSI BASED ON GCBN
A. ...
doi:10.1109/jstars.2021.3062642
fatcat:b4hjojt5lnhcfkow3kdkiwjs5u
Semisupervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images
2013
IEEE Transactions on Geoscience and Remote Sensing
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We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both illposed and poor-posed conditions ...
Experimental results on four real hyperspectral images demonstrate that the proposed method compares favorably with conventional feature extraction methods. ...
Landgrebe for providing the AVIRIS Indian Pines and Washington DC Mall data sets, Prof. Crawford for providing KSC and Botswana data sets, Prof. Cai for providing SDA source code, Prof. ...
doi:10.1109/tgrs.2012.2200106
fatcat:66cp43n5ofebppkmif3tijde74
Semisupervised Hyperspectral Image Classification via Neighborhood Graph Learning
2015
IEEE Geoscience and Remote Sensing Letters
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In many domains, such as remotely sensed hyperspectral image (HSI) classification, the data violates this assumption. ...
These approaches typically rely on a smoothness assumption such that examples that are similar in input space should also be similar in label space. ...
First, we assess the two proposed architectures for pairwise classification in order to verify that the constructed neighborhood graph makes sensible predictions. ...
doi:10.1109/lgrs.2015.2438227
fatcat:wddrmp3mn5f3jmagkinthj6tua
Overview of Hyperspectral Image Classification
2020
Journal of Sensors
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This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification. ...
Many methods have achieved good classification results in the classification of hyperspectral images. ...
Typical semisupervised classification methods include model generation algorithms, semisupervised support vector machines, graph-based semisupervised algorithms, and self-training, collaborative training ...
doi:10.1155/2020/4817234
fatcat:m6tbvti7tzghndgvg3zl6k7bqm
Semi-Supervised Dimensionality Reduction of Hyperspectral Image Based on Sparse Multi-Manifold Learning
2015
Journal of Computer and Communications
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In this paper, we proposed a new semi-supervised multi-manifold learning method, called semisupervised sparse multi-manifold embedding (S 3 MME), for dimensionality reduction of hyperspectral image data ...
relative importance to the labeled ones through a graph-based methodology. ...
The authors would like to thank the anonymous reviewers for their constructive advice. ...
doi:10.4236/jcc.2015.311006
fatcat:5b2ibfvilzejnav4rrveulspau
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art
2017
IEEE Geoscience and Remote Sensing Magazine
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Hence, rigorous and innovative methodologies are required for hyperspectral image and signal processing and have become a center of attention for researchers worldwide. ...
Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information, which make a plethora of applications for the ...
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
A Fast and Robust Sparse Approach for Hyperspectral Data Classification Using a Few Labeled Samples
2012
IEEE Transactions on Geoscience and Remote Sensing
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In this paper, we exploit certain special properties of hyperspectral data and propose an 1 -minimization-based sparse representation classification approach to overcome this difficulty in hyperspectral ...
Index Terms-Homotopy, hyperspectral data classification, remote sensing, sparse representation, 1 -minimization. ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. ...
doi:10.1109/tgrs.2011.2172617
fatcat:sg6xrpsxujfexlmumovqgtusp4
Semisupervised Hyperspectral Band Selection Via Spectral–Spatial Hypergraph Model
2015
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Band selection is an essential step towards effective and efficient hyperspectral image classification. ...
Then a linear regression model with group sparsity constraint is used for band selection. Finally, hyperspectral pixels with selected bands are used to train a support vector machine classifier. ...
Image Classification Finally, we perform image classification based on the selected bands which are the same for all samples. ...
doi:10.1109/jstars.2015.2443047
fatcat:wxtmoeoke5hobavexp7wdyyns4
A new framework for hyperspectral image classification using multiple semisupervised collaborative classification algorithm
2019
IEEE Access
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INDEX TERMS Active learning, hyperspectral image classification, semisupervised learning. ...
Hyperspectral images (HSIs) have evident advantages in image understanding because of enormous spectral bands, and rich spatial information. ...
SEMISUPERVISED LEARNING FOR HYPERSPECTRAL IMAGE CLASSIFICATION In hyperspectral image classification, during the past few decades, the semisupervised learning methods has drawn great interests in remote ...
doi:10.1109/access.2019.2933589
fatcat:2x6lmaxhovh7rbqg4ybilsykcy
JAGAN: A Framework for Complex Land Cover Classification Using Gaofen-5 AHSI Images
2022
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Thus, to highlight and distinguish effective features, we propose a hyperspectral classification framework based on a joint channel-space attention mechanism and generative adversarial network (JAGAN). ...
accuracy of 82.30%, indicating the JAGAN can effectively improve the classification accuracy for limited samples in complex regional environments using GF-5 AHSI images. ...
[59] proposed a semisupervised framework for hyperspectral images based on 1D-GAN with limited labeled samples. Wang et al. ...
doi:10.1109/jstars.2022.3144339
fatcat:v7o2ze6kpffxbdr3tdkj7wq4mi
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