@inproceedings{0ef1753224604855871a3f21de9b95b1,
title = "Farmland Change Monitoring Using Multi-Temporal SAR Data with Deep Learning",
abstract = "Accurate farmland change monitoring is crucial for achieving global 'Zero Hunger' under Sustainable Development Goal 2 (SDG2), proposed by the United Nations (UN). We propose a novel deep learning framework combining multi-temporal synthetic aperture radar (SAR) data with change detection techniques. SAR's all-weather imaging capabilities overcome optical data limitations. The Unpaired Image Augmentation (UIA) boosts training diversity and significantly improves detection metrics, achieving up to 95.29 \% Accuracy. The application in Jinan, China, highlights robust performance and scalability across multiple frames. The approach cost-effectively identifies subtle farmland conversions in near-real time, aiding sustainable agriculture and informed policymaking.",
keywords = "Change Detection, Deep Learning, Farmland, Synthetic Aperture Radar",
author = "Chaowei Jiang and Chao Wang and Fan Wu and Lu Xu and Nan Chen and Tianyang Li and Yixian Tang",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2025 ; Conference date: 03-08-2025 Through 08-08-2025",
year = "2025",
doi = "10.1109/IGARSS55030.2025.11314009",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "304--307",
booktitle = "IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium, Proceedings",
address = "美国",
}