2023
DOI: 10.1109/jstars.2022.3226758
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Hyperspectral Image Classification Based on 3-D Multihead Self-Attention Spectral–Spatial Feature Fusion Network

Abstract: Convolutional neural networks are a popular method in hyperspectral image classification. However, the accuracy of the models is closely related to the number and spatial size of training samples. Which relieve the performance decline by the number and spatial size of training samples, we designed a 3D multi-head self-attention spectral-spatial feature fusion network (3DMHSA-SSFFN) that contains step-by-step feature extracted blocks (SBSFE) and 3D multi-head-self-attention-module (3DMHSA). The proposed step-by… Show more

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Cited by 5 publications
(9 citation statements)
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“…employed 2D CNN and a transformer to gain joint spatial spectral features. Zhou et al 19 . used multi-scale convolution to mine features and combined multi-scale features to enhance feature expressiveness.…”
Section: Related Workmentioning
confidence: 99%
See 2 more Smart Citations
“…employed 2D CNN and a transformer to gain joint spatial spectral features. Zhou et al 19 . used multi-scale convolution to mine features and combined multi-scale features to enhance feature expressiveness.…”
Section: Related Workmentioning
confidence: 99%
“…15,16 To address these limitations, spectral-spatial methods have been proposed. In recent spectral-spatial methods, [17][18][19][20] CNNs are commonly employed to extract spectralspatial features from adjacent pixels, and convolution is an important component. [21][22][23] Recently, attention mechanisms were developed by simulating the visual system of humans, which selectively concentrates on prominent parts rather than handling each part consistently.…”
Section: Introductionmentioning
confidence: 99%
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“…Hyperspectral images (HSIs) are data cubes captured by hyperspectral sensors, which simultaneously reveal 2-D spatial and 1-D spectral information about land cover substances [1]. What distinguishes HSIs from panchromatic and multispectral images is that their pixels record the distinctive spectral signatures using hundreds of nearly continuous spectral bands [2][3][4]. The high-resolution spectral response curves reflect detailed characteristics of land cover substances [5].…”
Section: Introductionmentioning
confidence: 99%
“…However, the above-mentioned spatial attention modules generally deduce a few modes of attention. To express possible spatial dependency sufficiently, transformers [194,195], which originate from the field of natural language processing and have been the core component of the ChatGPT model [196], adopt multi-head SA (MHSA) modules [181,[197][198][199] to integrate various types of attention from different subspaces into a linear representation [200][201][202]. Transformer is also good at handling long-distance spectral dependency.…”
Section: Introductionmentioning
confidence: 99%