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SE-ResUNet Using Feature Combinations: A Deep Learning Framework for Accurate Mountainous Cropland Extraction Using Multi-Source Remote Sensing Data

Author

Listed:
  • Ling Xiao

    (Faculty of Geography, Yunnan Normal University, Kunming 650500, China
    The Engineering Research Center of GIS Technology in Western China of Ministry of Education of China, Yunnan Normal University, Kunming 650500, China)

  • Jiasheng Wang

    (Faculty of Geography, Yunnan Normal University, Kunming 650500, China
    The Engineering Research Center of GIS Technology in Western China of Ministry of Education of China, Yunnan Normal University, Kunming 650500, China)

  • Kun Yang

    (Faculty of Geography, Yunnan Normal University, Kunming 650500, China
    The Engineering Research Center of GIS Technology in Western China of Ministry of Education of China, Yunnan Normal University, Kunming 650500, China)

  • Hui Zhou

    (Faculty of Geography, Yunnan Normal University, Kunming 650500, China
    The Engineering Research Center of GIS Technology in Western China of Ministry of Education of China, Yunnan Normal University, Kunming 650500, China)

  • Qianwen Meng

    (Faculty of Geography, Yunnan Normal University, Kunming 650500, China
    The Engineering Research Center of GIS Technology in Western China of Ministry of Education of China, Yunnan Normal University, Kunming 650500, China)

  • Yue He

    (Faculty of Geography, Yunnan Normal University, Kunming 650500, China
    The Engineering Research Center of GIS Technology in Western China of Ministry of Education of China, Yunnan Normal University, Kunming 650500, China)

  • Siyi Shen

    (Faculty of Geography, Yunnan Normal University, Kunming 650500, China
    The Engineering Research Center of GIS Technology in Western China of Ministry of Education of China, Yunnan Normal University, Kunming 650500, China)

Abstract

The accurate extraction of mountainous cropland from remote sensing images remains challenging due to its fragmented plots, irregular shapes, and the terrain-induced shadows. To address this, we propose a deep learning framework, SE-ResUNet, that integrates Squeeze-and-Excitation (SE) modules into ResUNet to enhance feature representation. Leveraging Sentinel-1/2 imagery and DEM data, we fuse vegetation indices (NDVI/EVI), terrain features (Slope/TRI), and SAR polarization characteristics into 3-channel inputs, optimizing the network’s discriminative capacity. Comparative experiments on network architectures, feature combinations, and terrain conditions demonstrated the superiority of our approach. The results showed the following: (1) feature fusion (NDVI + TerrainIndex + SAR) had the best performance (OA: 97.11%; F1-score: 96.41%; IoU: 93.06%), significantly reducing shadow/cloud interference. (2) SE-ResUNet outperformed ResUNet by 3.53% for OA and 8.09% for IoU, emphasizing its ability to recalibrate channel-wise features and refine edge details. (3) The model exhibited robustness across diverse slopes/aspects (OA > 93.5%), mitigating terrain-induced misclassifications. This study provides a scalable solution for mountainous cropland mapping, supporting precision agriculture and sustainable land management.

Suggested Citation

  • Ling Xiao & Jiasheng Wang & Kun Yang & Hui Zhou & Qianwen Meng & Yue He & Siyi Shen, 2025. "SE-ResUNet Using Feature Combinations: A Deep Learning Framework for Accurate Mountainous Cropland Extraction Using Multi-Source Remote Sensing Data," Land, MDPI, vol. 14(5), pages 1-23, April.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:5:p:937-:d:1642552
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    References listed on IDEAS

    as
    1. Haoran Meng & Cunjun Li & Yu Liu & Yusheng Gong & Wanying He & Mengxi Zou, 2023. "Corn Land Extraction Based on Integrating Optical and SAR Remote Sensing Images," Land, MDPI, vol. 12(2), pages 1-17, February.
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