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S2IT: Spectral-Spatial Interactive Transformer for Hyperspectral Image Classification
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DOI：10.1109/LGRS.2024.3449238.png)
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Abstract
En 中文
Hyperspectral image (HSI) encompasses a wealth of spectral-spatial information, offering a sufficient foundation for classification. However, the presence of redundancy poses challenges for achieving accurate classification. In this letter, we design a spectral-spatial interactive transformer (S2IT) for HSI classification (HSIC). S2IT commences with a meticulously designed spectral-spatial reconstruction (S2R) module, which aims to augment the representation of shallow features. Subsequently, an adaptive asymmetric gating mechanism transformer (AGM-Former) aims to delve into and extract comprehensive local-global features from HSI. Ultimately, the spectral-spatial interactive attention (S2IA) synergizes the spectral-spatial features and enhances classification prowess. S2IT demonstrates rigorous experiments on three renowned datasets: Houston2013 (HU), Indian Pines (IP), and the University of Pavia (UP), which validates its effectiveness in enhancing HSIC accuracy.
Keywords:
Convolutional neural networks
Sun
Convolutional neural network (CNN)
hyperspectral image (HSI)
spectral-spatial features
vision transformer (ViT)
Sun
Convolutional neural network (CNN)
hyperspectral image (HSI)
spectral-spatial features
vision transformer (ViT)
Journal
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