Semisupervised Hyperspectral Band Selection Based on Dual-Constrained Low-Rank Representation

Yu, Chunyan; Zhou, Sijia; Song, Ming; Chang, Chein‐I

doi:10.1109/lgrs.2021.3049267

Cited by 15 publications

(6 citation statements)

References 16 publications

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“…To reveal DMuCA in key bands perception, we further perform HSI classification by DMuCA with full or selected bands (reference the results from [40,41]) as the input, and compare it with the ResNet base (Base). The results in Table 6 show that DMuCA achieves a significant increase over the base when performing classification with the full spectral bands, and a slight increase with the selected bands.…”

Section: Ablation Studymentioning

confidence: 99%

A Dual Multi-Head Contextual Attention Network for Hyperspectral Image Classification

Liang

et al. 2022

Remote Sensing

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To learn discriminative features, hyperspectral image (HSI), containing 3-D cube data, is a preferable means of capturing multi-head self-attention from both spatial and spectral domains if the burden in model optimization and computation is low. In this paper, we design a dual multi-head contextual self-attention (DMuCA) network for HSI classification with the fewest possible parameters and lower computation costs. To effectively capture rich contextual dependencies from both domains, we decouple the spatial and spectral contextual attention into two sub-blocks, SaMCA and SeMCA, where depth-wise convolution is employed to contextualize the input keys in the pure dimension. Thereafter, multi-head local attentions are implemented as group processing when the keys are alternately concatenated with the queries. In particular, in the SeMCA block, we group the spatial pixels by evenly sampling and create multi-head channel attention on each sampling set, to reduce the number of the training parameters and avoid the storage increase. In addition, the static contextual keys are fused with the dynamic attentional features in each block to strengthen the capacity of the model in data representation. Finally, the decoupled sub-blocks are weighted and summed together for 3-D attention perception of HSI. The DMuCA module is then plugged into a ResNet to perform HSI classification. Extensive experiments demonstrate that our proposed DMuCA achieves excellent results over several state-of-the-art attention mechanisms with the same backbone.

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Section: Ablation Studymentioning

confidence: 99%

A Dual Multi-Head Contextual Attention Network for Hyperspectral Image Classification

Liang

et al. 2022

Remote Sensing

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“…Hyperspectral images typically include a large amount of approximately continuous spectral band information and spatial location information [1][2][3]. HSI classification distinguishes the corresponding categories of each pixel, which is a basic and key application technology in remote sensing and which can be successfully utilized in numerous fields such as mineral detection, environment detection, and crop monitoring [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Based on Adaptive Global–Local Feature Fusion

Yang,

Kong,

Wang

et al. 2024

Remote Sensing

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Labeled hyperspectral image (HSI) information is commonly difficult to acquire, so the lack of valid labeled data becomes a major puzzle for HSI classification. Semi-supervised methods can efficiently exploit unlabeled and labeled data for classification, which is highly valuable. Graph-based semi-supervised methods only focus on HSI local or global data and cannot fully utilize spatial–spectral information; this significantly limits the performance of classification models. To solve this problem, we propose an adaptive global–local feature fusion (AGLFF) method. First, the global high-order and local graphs are adaptively fused, and their weight parameters are automatically learned in an adaptive manner to extract the consistency features. The class probability structure is then used to express the relationship between the fused feature and the categories and to calculate their corresponding pseudo-labels. Finally, the fused features are imported into the broad learning system as weights, and the broad expansion of the fused features is performed with the weighted broad network to calculate the model output weights. Experimental results from three datasets demonstrate that AGLFF outperforms other methods.

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“…In the field of machine learning, semi-supervised learning acquires knowledge and experience from a small number of labeled samples. Mining usable information from a large number of unlabeled samples helps the classification model to train and improve the classification accuracy (Yin et al, 2021;Yu et al, 2021;Chen et al, 2020 ). Therefore, a large number of scholars have carried out the research of semi-supervised learning in remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Sample labeling and classification method of hyperspectral remote sensing images based on texture features and semi-supervised learning

Zhou

Gong

et al. 2023

Preprint

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Abstract. Hyperspectral images contain abundant spectral and spatial information about the earth's surface, labeling data processing and analysis more difficult, as well as the problem of sample labeling. In this paper, local binary pattern (LBP), sparse representation and mixed logistic regression model are introduced, and a sample labeling method based on neighborhood information and priority classifier discrimination is presented. Then, a hyperspectral remote sensing image classification method based on texture features and semi-supervised learning is implemented. The LBP is employed to extract features of spatial texture information from remote sensing images and enrich the feature information of samples. Then the multivariate logistic regression model is used to select the unlabeled samples with the largest amount of information, and the unlabeled samples with neighborhood information and priority classifier tags are selected to obtain the pseudo-labeled samples after learning. By making full use of the advantages of sparse representation and mixed logistic regression model, a new hyperspectral remote sensing image classification model based on semi-supervised learning is constructed to effectively achieve accurate classification of hyperspectral images. The data of Indian Pines, Salinas scene and Pavia University are selected to verify the validity of the proposed method. The experiment results show that the proposed classification method can obtain higher classification accuracy and show stronger timeliness and generalization ability.

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Semisupervised Hyperspectral Band Selection Based on Dual-Constrained Low-Rank Representation

Cited by 15 publications

References 16 publications

A Dual Multi-Head Contextual Attention Network for Hyperspectral Image Classification

A Dual Multi-Head Contextual Attention Network for Hyperspectral Image Classification

Hyperspectral Image Classification Based on Adaptive Global–Local Feature Fusion

Sample labeling and classification method of hyperspectral remote sensing images based on texture features and semi-supervised learning

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