Expansion Spectral–Spatial Attention Network for Hyperspectral Image Classification

Wang, Shuo; Liu, Zhengjun; Chen, Yiming; Hou, Chengcheng; Liu, Aixia; Zhang, Zhenbei

doi:10.1109/jstars.2023.3288521

Cited by 6 publications

(2 citation statements)

References 48 publications

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“…To address these issues raised regarding CNNs, such as parameter redundancy and a limited ability to handle spatial and scale variations, researchers have introduced various CNN variants to improve the models and enhance the performance and adaptability of CNNs. Wang et al [34] proposed a model known as the expansion spectral-spatial attention network. It introduces expansion convolution as a dual-branch feature extraction unit, which expands the receptive field of input patches, enhancing the perception of large-scale features.…”

Section: Introductionmentioning

confidence: 99%

Dilated Spectral–Spatial Gaussian Transformer Net for Hyperspectral Image Classification

Zhang,

Wang,

Zhang

2024

Remote Sensing

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In recent years, deep learning-based classification methods for hyperspectral images (HSIs) have gained widespread popularity in fields such as agriculture, environmental monitoring, and geological exploration. This is owing to their ability to automatically extract features and deliver outstanding performance. This study provides a new Dilated Spectral–Spatial Gaussian Transformer Net (DSSGT) model. The DSSGT model incorporates dilated convolutions as shallow feature extraction units, which allows for an expanded receptive field while maintaining computational efficiency. We integrated transformer architecture to effectively capture feature relationships and generate deep fusion features, thereby enhancing classification accuracy. We used consecutive dilated convolutional layers to extract joint low-level spectral–spatial features. We then introduced Gaussian Weighted Pixel Embedding blocks, which leverage Gaussian weight matrices to transform the joint features into pixel-level vectors. By combining the features of each pixel with its neighbouring pixels, we obtained pixel-level representations that are more expressive and context-aware. The transformed vector matrix was fed into the transformer encoder module, enabling the capture of global dependencies within the input data and generating higher-level fusion features with improved expressiveness and discriminability. We evaluated the proposed DSSGT model using five hyperspectral image datasets through comparative experiments. Our results demonstrate the superior performance of our approach compared to those of current state-of-the-art methods, providing compelling evidence of the DSSGT model’s effectiveness.

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Section: Introductionmentioning

confidence: 99%

Dilated Spectral–Spatial Gaussian Transformer Net for Hyperspectral Image Classification

Zhang,

Wang,

Zhang

2024

Remote Sensing

Self Cite

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“…However, they treat all features equally without considering different significances. Moreover, capturing global contextual information and establishing long-range dependencies can be inefficiently limited by their inherent structure [36]. (2) Transformers are good at global interaction and capturing salient features.…”

Section: Introductionmentioning

confidence: 99%

A U-Shaped Convolution-Aided Transformer with Double Attention for Hyperspectral Image Classification

Qin,

Wang,

et al. 2024

Remote Sensing

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Convolutional neural networks (CNNs) and transformers have achieved great success in hyperspectral image (HSI) classification. However, CNNs are inefficient in establishing long-range dependencies, and transformers may overlook some local information. To overcome these limitations, we propose a U-shaped convolution-aided transformer (UCaT) that incorporates convolutions into a novel transformer architecture to aid classification. The group convolution is employed as parallel local descriptors to extract detailed features, and then the multi-head self-attention recalibrates these features in consistent groups, emphasizing informative features while maintaining the inherent spectral–spatial data structure. Specifically, three components are constructed using particular strategies. First, the spectral groupwise self-attention (spectral-GSA) component is developed for spectral attention, which selectively emphasizes diagnostic spectral features among neighboring bands and reduces the spectral dimension. Then, the spatial dual-scale convolution-aided self-attention (spatial-DCSA) encoder and spatial convolution-aided cross-attention (spatial-CCA) decoder form a U-shaped architecture for per-pixel classifications over HSI patches, where the encoder utilizes a dual-scale strategy to explore information in different scales and the decoder adopts the cross-attention for information fusion. Experimental results on three datasets demonstrate that the proposed UCaT outperforms the competitors. Additionally, a visual explanation of the UCaT is given, showing its ability to build global interactions and capture pixel-level dependencies.

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A multiscale dilated attention network for hyperspectral image classification

Tu,

Liu,

Jiang

et al. 2024

Advances in Space Research

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Expansion Spectral–Spatial Attention Network for Hyperspectral Image Classification

Cited by 6 publications

References 48 publications

Dilated Spectral–Spatial Gaussian Transformer Net for Hyperspectral Image Classification

Dilated Spectral–Spatial Gaussian Transformer Net for Hyperspectral Image Classification

A U-Shaped Convolution-Aided Transformer with Double Attention for Hyperspectral Image Classification

A multiscale dilated attention network for hyperspectral image classification

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