A large-scale VHR parcel dataset and a novel hierarchical semantic boundary-guided network for agricultural parcel delineation

IF 10.6 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL
Hang Zhao , Bingfang Wu , Miao Zhang , Jiang Long , Fuyou Tian , Yan Xie , Hongwei Zeng , Zhaoju Zheng , Zonghan Ma , Mingxing Wang , Junbin Li
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引用次数: 0

Abstract

Current agricultural parcels (AP) extraction faces two main limitations: (1) existing AP delineation methods fail to fully utilize low-level information (e.g., parcel boundary information), leading to unsatisfactory performance under certain circumstances; (2) the lack of large-scale, high-resolution AP benchmark datasets in China hinders comprehensive model evaluation and improvement. To address the first limitation, we develop a hierarchical semantic boundary-guided network (HBGNet) to fully leverage boundary semantics, thereby improving AP delineation. It integrates two branches, a core branch of AP feature extraction and an auxiliary branch related to boundary feature mining. Specifically, the boundary extract branch employes a module based on Laplace convolution operator to enhance the model’s awareness of parcel boundary. For AP feature extraction, a local–global context aggregation module is designed to enhance the semantic representation of AP, improving the adaptability across different AP scenarios. Meanwhile, a boundary-guided module is developed to enhance boundary details of high-level AP semantic information. Ultimately, a multi-grained feature fusion module is designed to enhance the capacity of HBGNet to extract APs with various sizes and shapes. Regarding the second limitation, we construct the first large-scale very high-resolution (VHR) agricultural parcel dataset (FHAPD) across seven different areas, covering more than 10,000 km2, using data from GaoFen-1 (2-meter) and GaoFen-2 (1-meter). Detailed experiments are conducted on the FHAPD, a publicly European dataset (i.e., Al4boundaries), and medium-resolution Sentinel-2 images from the Netherlands and HBGNet is compared with other eight AP delineation methods. Results show that HBGNet outperforms the other eight methods in attribute and geometry accuracy. The Intersection over Union (IOU), F1-score of the boundary (Fbdy), and global total-classification (GTC) exceed other methods by 0.61 %-7.52 %, 0.8 %-36.3 %, and 1.7 %-31.8 %, respectively. It also effectively transfers to unseen regions. We conclude that the proposed HBGNet is an effective, advanced, and transferable method for diverse agricultural scenarios and remote sensing images.
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来源期刊
ISPRS Journal of Photogrammetry and Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing 工程技术-成像科学与照相技术
CiteScore
21.00
自引率
6.30%
发文量
273
审稿时长
40 days
期刊介绍: The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive. P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields. In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.
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