ORIGINAL RESEARCH article
Front. Remote Sens.
Sec. Image Analysis and Classification
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1583560
RCTNet: Residual conv-attention transformer network for corn hyperspectral image classification
Provisionally accepted- Henan Institute of Science and Technology, Xinxiang, China
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Classifying corn varieties presents a significant challenge due to the high-dimensional characteristics of hyperspectral images and the complexity of feature extraction, which hinder progress in developing intelligent agriculture systems. To cope with these challenges, we introduce the Residual Convolution-Attention Transformer Network (RCTNet), an innovative framework designed to optimize hyperspectral image classification. RCTNet integrates Conv2D with Channel Attention (2DWCA) and Conv3D with Spatial Attention (3DWSA) modules for efficient local spatial-spectral feature extraction, ensuring meaningful feature selection across multiple dimensions. Additionally, a residual transformer module is incorporated to enhance global feature learning by capturing long-range dependencies and improving classification performance. By effectively fusing local and global representations, RCTNet maximizes feature utilization, leading to superior accuracy and robustness in classification tasks. Extensive experimental results on a corn seed hyperspectral image dataset and two widely used remote sensing datasets validate the effectiveness, efficiency, and generalizability of RCTNet in hyperspectral image classification applications.
Keywords: hyperspectral image classification, Corn seed, Intelligent agriculture, Corn identification, deep learning
Received: 26 Feb 2025; Accepted: 09 Jun 2025.
Copyright: © 2025 Li, Li, Chen, Li, Jin and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yan Li, Henan Institute of Science and Technology, Xinxiang, China
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