Spatial-Spectral Decoupling Framework for Hyperspectral Image Classification

Fang, Jie; Zhu, Zijiang; He, Guanghua; Wang, Nan; Cao, Xiaoqian

doi:10.1109/lgrs.2023.3277347

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…4, we can conclude that all parameters have an impact on the proposed method. When we explore the influence of the ration k, we fix the dual-window size as (5,7), and the tradeoff parameters are set to (0.1,1,1). Then, we fix the parameter with the best AUC value in the last round and then explore the optimal value of the next parameter.…”

Section: Parameter Analysismentioning

confidence: 99%

“…1 Compared with visible and multispectral imagery, hyperspectral images (HSIs) contain hundreds of continuous spectral bands that provide spectral-spatial information. On this basis, HSI interpretation techniques have been widely applied in many fields, such as classification, [2][3][4][5] change detection, [6][7][8][9] and object detection. [10][11][12] As an important subbranch of hyperspectral target location, hyperspectral anomaly detection (HAD) aims to distinguish objects of interest by capturing subtle spectral differences of ground objects without any prior information or supervision.…”

Section: Introductionmentioning

confidence: 99%

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Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection

Wang,

Shi,

Cheng

et al. 2023

J. Appl. Rem. Sens.

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Hyperspectral anomaly detection (HAD) is a technique to find observations without prior knowledge, which is of particular interest as a branch of remote sensing object detection. However, the application of HAD is limited by various challenges, such as high-dimensional data, high intraclass variability, redundant information, and limited samples. To overcome these restrictions, we report an unsupervised strategy to implement HAD by dimensionality reduction (DR) and prior-based collaborative representation with adaptive global salient weight. The proposed framework includes three main steps. First, we select the most discriminating bands as the input hyperspectral images for subsequent processing in a DR manner. Then, we apply piecewise-smooth prior and local salient prior to collaborative representation to produce the initial detection map. Finally, to generate the final detection map, a global adaptive salient map is applied to the initial anomaly map to further highlight anomalies. Most importantly, the experimental results show that the proposed method outperforms alternative detectors on several datasets over different scenes. In particular, on the Gulfport dataset, the area under the curve value obtained by the proposed method is 0.9932, which is higher than the second-best method, convolutional neural network detector, by 0.0071.

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Section: Parameter Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection

Wang,

Shi,

Cheng

et al. 2023

J. Appl. Rem. Sens.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, the extraction of spectral-spatial features can be realized through multichannel fusion, as exemplified by two-channel CNN (TCCNN) [36] and multi-channel CNN (MCCNN) [37]. Furthermore, inspired by the visual attention mechanism, attention techniques have been integrated into CNNs in various recent methods [38,39]. While existing CNN-based methods have made significant performance strides, the convolution operations in CNN models operate on regular square regions, neglecting pixel-to-pixel relationships.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Pooling-Based Graph Attention U-Net for Hyperspectral Image Classification

Diao,

Dai,

Wang

et al. 2024

Remote Sensing

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In recent years, graph convolutional networks (GCNs) have attracted increasing attention in hyperspectral image (HSI) classification owing to their exceptional representation capabilities. However, the high computational requirements of GCNs have led most existing GCN-based HSI classification methods to utilize superpixels as graph nodes, thereby limiting the spatial topology scale and neglecting pixel-level spectral–spatial features. To address these limitations, we propose a novel HSI classification network based on graph convolution called the spatial-pooling-based graph attention U-net (SPGAU). Specifically, unlike existing GCN models that rely on fixed graphs, our model involves a spatial pooling method that emulates the region-growing process of superpixels and constructs multi-level graphs by progressively merging adjacent graph nodes. Inspired by the CNN classification framework U-net, SPGAU’s model has a U-shaped structure, realizing multi-scale feature extraction from coarse to fine and gradually fusing features from different graph levels. Additionally, the proposed graph attention convolution method adaptively aggregates adjacency information, thereby further enhancing feature extraction efficiency. Moreover, a 1D-CNN is established to extract pixel-level features, striking an optimal balance between enhancing the feature quality and reducing the computational burden. Experimental results on three representative benchmark datasets demonstrate that the proposed SPGAU outperforms other mainstream models both qualitatively and quantitatively.

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Kernel Tensor Sparse Coding Model for Precise Crop Classification of UAV Hyperspectral Image

Yang,

Zhang,

Bao

et al. 2023

IEEE Geosci. Remote Sensing Lett.

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Spatial-Spectral Decoupling Framework for Hyperspectral Image Classification

Cited by 3 publications

References 19 publications

Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection

Prior-based collaborative representation with global adaptive weight for hyperspectral anomaly detection

Spatial-Pooling-Based Graph Attention U-Net for Hyperspectral Image Classification

Kernel Tensor Sparse Coding Model for Precise Crop Classification of UAV Hyperspectral Image

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