Laser & Optoelectronics Progress, Volume. 60, Issue 12, 1210012(2023)
Hyperspectral Image Classification Based on Dual-Channel Feature Enhancement
A classification method of hyperspectral images based on dual channel feature enhancement (DCFE) is proposed to solve the problem of how to extract and use the spatial and spectral information of hyperspectral images more fully when the training samples are limited. First, two channels are designed to capture spectral and spatial features, and 3D convolution is used as a feature extractor in each channel. The feature map from the reduced-dimension spectral channel is fused with the feature map of the spatial channel. Finally, the feature map combining spectral and spatial features is input into the attention module, and feature enhancement is achieved by increasing attention to important information while decreasing interference from irrelevant information. The experimental results show that the proposed method has an overall classification accuracy of 96.57%, 98.15%, 98.95%, and 96.83% on four hyperspectral data sets, including Indian Pines (3% training sample), Pavia University (0.5% training sample), Salinas (0.5% training sample), and Botswana (1.2% training sample), respectively. When compared to the other five hyperspectral classification methods, the proposed method has remarkably improved the classification performance.
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Li Zhao, Leiquan Wang, Junsan Zhang, Zhimin Shao, Jie Zhu. Hyperspectral Image Classification Based on Dual-Channel Feature Enhancement[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1210012
Category: Image Processing
Received: May. 17, 2022
Accepted: Jun. 16, 2022
Published Online: Jun. 5, 2023
The Author Email: Zhang Junsan (zhangjunsan@upc.edu.cn)