Edge-Guided Dual-Stream Network for Plastic Greenhouse Extraction From Remote Sensing Image

• Published in: IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-21
• Main Authors: Zhang, Xiaoping, Cheng, Bo, Liang, Chenbin, Wang, Guizhou
• Format: Article
• Language: English
• Subjects: Accuracy; Body extraction; Data mining; edge detection; Feature extraction; Green products; high-resolution remote sensing images (HRRSIs); Image edge detection; plastic greenhouses (PGs); Plastics; Remote sensing; Semantic segmentation; Semantics; Transformers
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2024.3477290

Plastic greenhouses (PGs), as an important part of modernized facility-based agriculture, are widely used worldwide to improve crop yield. To facilitate precision agricultural management and environment assessment, there has been widespread research attention that focuses on accurately mapping the spatial distribution and exploring coverage of PGs via remote sensing and deep learning techniques. However, limited by PGs' poor intraclass similarity and interclass separability in high-resolution remote sensing images (HRRSIs), existing methods for PGs extraction are hard to achieve promising performance. Besides, their visualization results also suffer from adhesion problems in densely distributed areas, due to the lack of attention to geometric and boundary information. Therefore, this article proposes a novel edge-guided dual stream network (EDSNet), which consists of an edge detection branch, a body extraction branch, and an adaptive fusion module. In the body extraction branch, a new feature extractor, termed geometric-refined attention module (GRAM), is embedded to enhance the geometric information of PGs. Moreover, an edge-guided module (EGM) is introduced to guide the edge detection branch to gradually recover precise boundary details. Finally, the adaptive semantic fusion module (ASFM) adaptively fuses enhanced edge features and body features to maximally facilitate the information interaction between dual branches, thus improving interclass separability on both sides of edges while suppressing meaningless information outside bodies. The proposed EDSNet is validated on self-labeled and publicly available datasets, and the results demonstrate that this method performs well and is suitable for accurate extraction of PGs. The code will be available at https://github.com/xchouzhang/EDSNet .