Remote sensing crop classification model integrating 1D convolutional residual blocks and temporal attention

Zhengwu Yuan; Wang Chen; Aixia Yang; Wen Shao

doi:10.1117/12.3028952

22 May 2024 Remote sensing crop classification model integrating 1D convolutional residual blocks and temporal attention

Zhengwu Yuan, Wang Chen, Aixia Yang, Wen Shao

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Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 1317622 (2024) https://doi.org/10.1117/12.3028952
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

Crop type recognition in remote sensing plays a crucial role in agricultural management and resource monitoring. In addressing the challenge of temporal feature extraction from remote sensing time series data, this study introduces a novel temporal extraction module that integrates 1D convolutional residual blocks and temporal attention mechanisms. Our approach effectively captures temporal patterns in crop growth processes and achieves high-precision crop classification in remote sensing imagery. Through experimental validation on the Sentinel2-Agri dataset, our model demonstrates performance improvements in classification accuracy compared to traditional methods, yielding higher accuracy and finer-grained segmentation results. Additionally, ablation studies and exploration of model configurations substantiate the effectiveness and robustness of our approach. This research holds important implications for the advancement of remote sensing-based crop classification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhengwu Yuan, Wang Chen, Aixia Yang, and Wen Shao "Remote sensing crop classification model integrating 1D convolutional residual blocks and temporal attention", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 1317622 (22 May 2024); https://doi.org/10.1117/12.3028952

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KEYWORDS

Convolution

Feature extraction

Remote sensing

Performance modeling

Satellites

Image classification

Satellite imaging

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