A Refined Terrace Extraction Method Based on a Local Optimization Model Using GF-2 Images.

Kan, Guobin; Gong, Jie; Wang, Bao; Li, Xia; Shi, Jing; Ma, Yutao; Wei, Wei; Zhang, Jun

Terraces are an important form of surface modification, and their spatial distribution data are of utmost importance for ensuring food and water security. However, the extraction of terrace patches faces challenges due to the complexity of the terrain and limitations in remote sensing (RS) data. Therefore, there is an urgent need for advanced technology models that can accurately extract terraces. High-resolution RS data allows for detailed characterization of terraces by capturing more precise surface features. Moreover, leveraging deep learning (DL) models with local adaptive improvements can further enhance the accuracy of interpretation by exploring latent information. In this study, we employed five models: ResU-Net, U-Net++, RVTransUNet, XDeepLabV3+, and ResPSPNet as DL models to extract fine patch terraces from GF-2 images. We then integrated morphological, textural, and spectral features to optimize the extraction process by addressing issues related to low adhesion and edge segmentation performance. The model structure and loss function were adjusted accordingly to achieve high-quality terrace mapping results. Finally, we utilized multi-source RS data along with terrain elements for correction and optimization to generate a 1 m resolution terrace distribution map in the Zuli River Basin (TDZRB). Evaluation results after correction demonstrate that our approach achieved an OA, F1-Score, and MIoU of 96.67%, 93.94%, and 89.37%, respectively. The total area of terraces in the Zuli River Basin was calculated at 2557 ± 117.96 km2 using EM with our model methodology; this accounts for approximately 41.74% ± 1.93% of the cultivated land area within the Zuli River Basin. Therefore, obtaining accurate information on patch terrace distribution serves as essential foundational data for terrace ecosystem research and government decision-making.

REMOTE sensing; WATERSHEDS; WATER security; DEEP learning; DISTANCE education

Remote Sensing, 2025, Vol 17, Issue 1, p12

2072-4292

Academic Journal

10.3390/rs17010012