数字农科院2.0

Ensemble modelling based on transfer learning for enhancing crop mapping through synergistic integration of InSAR coherence and multispectral satellite data

文献类型： 外文期刊

作者： Liu, Niantang;Zhao, Qunshan;Williams, Richard;Duan, Si-Bo;Sun, Yingwei;Barrett, Brian

作者机构：

关键词： Crop mapping;InSAR;Coherence;Deep learning;Transfer learning;Feature importance

期刊名称： COMPUTERS AND ELECTRONICS IN AGRICULTURE

ISSN： 0168-1699

年卷期： 2025 年 242 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Recent advancements in remote sensing have enabled the integration of multi-temporal and multi-modal data for agricultural applications, such as crop mapping. This study proposes an innovative framework that explores the synergistic use of multi-temporal Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) coherence alongside Sentinel-2 and RapidEye multispectral data to enhance crop mapping in smallholder croplands in Bei'an county, China. Various deep learning models were evaluated, including the 3-Dimensional U-Net (3D UNet), Transformer, Attention-based Long Short-Term Memory (AtLSTM), and a baseline machine learning Random Forest (RF) model, focusing on their transfer learning capabilities in complex intercropping patterns. Our new architecture, Transformer-AtLSTM-RF, uses ensemble learning to fuse features from different classifiers with a rule-based strategy, facilitating multi-source feature fusion for enhanced crop classification performance. Fine-tuning with region-specific data yielded high overall accuracy (OA), mean F1 score, and mean intersection over union (mIoU) for two test sites: site A (OA: 96.2%, mean F1: 92.7%, mIoU: 86.9%) and site B (OA: 90.7%, mean F1: 88.6%, mIoU: 79.7%). Additionally, we assessed feature importance by visualizing critical temporal features during the model inference process to improve an in-depth understanding of underlying patterns in the feature learning process. Our findings demonstrate the effectiveness of integrating time series SAR-derived and optical data with advanced models for mapping intercropping systems.

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