A Dual-Branch Framework Integrating the Segment Anything Model and Semantic-Aware Network for High-Resolution Cropland Extraction

作者：Dujuan Zhang, Yi‐Ping Li, Yucai Shen, Hengliang Guo, Haitao Wei, Jian Cui, Gang Wu, Tian He, Lingling Wang, Xiangdong Liu, Shan Zhao · 发表于：Remote Sensing · 年份：2025 · DOI：10.3390/rs17203424 · 被引用次数：3 · 研究领域：Smart Agriculture and AI

Accurate spatial information of cropland is crucial for precision agricultural management and ensuring national food security. High-resolution remote sensing imagery combined with deep learning algorithms provides a promising approach for extracting detailed cropland information. However, due to the diverse morphological characteristics of croplands across different agricultural landscapes, existing deep learning methods encounter challenges in precise boundary localization. The advancement of large-scale vision models has led to the emergence of the Segment Anything Model (SAM), which has demonstrated remarkable performance on natural images and attracted considerable attention in the field of remote sensing image segmentation. However, when applied to high-resolution cropland extraction, SAM faces limitations in semantic expressiveness and cross-domain adaptability. To address these issues, this study proposes a dual-branch framework integrating SAM and a semantically aware network (SAM-SANet) for high-resolution cropland extraction. Specifically, a semantically aware branch based on a semantic segmentation network is applied to identify cropland areas, complemented by a boundary-constrained SAM branch that directs the model’s attention to boundary information and enhances cropland extraction performance. Additionally, a boundary-aware feature fusion module and a prompt generation and selection module are incorporated into the SAM branch for precise cropland boundary locali...