Transactions of Nanjing University of Aeronautics & Astronautics
A Hyperspectral Image Classification Based on Spectral Band Graph Convolutional and Attention-Enhanced CNN Joint Network
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Abstract:
Hyperspectral image （HSI） classification is crucial for numerous remote sensing applications. Traditional deep learning methods may miss pixel relationships and context， leading to inefficiencies. This paper introduces the spectral band graph convolutional and attention-enhanced CNN joint network （SGCCN）， a novel approach that harnesses the power of spectral band graph convolutions for capturing long-range relationships， utilizes local perception of attention-enhanced multi-level convolutions for local spatial feature and employs a dynamic attention mechanism to enhance feature extraction. The SGCCN integrates spectral and spatial features through a self-attention fusion network， significantly improving classification accuracy and efficiency. The proposed method outperforms existing techniques， demonstrating its effectiveness in handling the challenges associated with HSI data.
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This work was supported in part by the National Natural Science Foundations of China （No. 61801214）， and the Postgraduate Research Practice Innovation Program of NUAA （No.xcxjh20231504）.
Clc Number:
TP751
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