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Hyperspectral Image Classification Based on Spectral-Spatial Attention Tensor Network
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DOI：10.1109/LGRS.2023.3332600.png)
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Abstract
En 中文
As an essential task in remote sensing, hyperspectral image classification (HSIC) has been extensively studied. Although many methods based on deep learning have been inducted to improve the performance of HSIC, they still face two challenges: 1) Can the joint spectral-spatial information in the hyperspectral image (HSI) be effectively utilized? 2) Does each pixel in a certain sample contribute equally to classification? We exploit a spectral-spatial attention tensor network (SSATN) in this letter, where the coordinate attention (CA) mechanism is first introduced into a full tensor network. We present a learnable tensor squeezing projection layer (TSPL) instead of the classical pooling layer for the CA block, which enables the network to selectively focus on discriminative features in spectral and spatial. Besides, the SSATN accepts raw HSI data as the input without dimensionality reduction in advance. Compared with the state-of-the-art convolutional neural network (CNN) methods, SSATN can effectively capture certain spectral-spatial features via tensor transformation with fewer parameters. The experimental results on two widely used HSI datasets prove the advantage of the SSATN method.
Keywords:
Tensors
Feature extraction
Task analysis
Convolutional neural networks
Transformers
Image classification
Hyperspectral imaging
Hyperspectral image classification (HSIC)
spectral-spatial attention
tensor network
Journal
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5.1K