Work

RBSF-Net: A Refined Boundary-Semantic Fusion Network Based on Marker-Controlled Watershed for Delineating Agricultural Fields From High-Resolution Satellite Imagery

Year: 2025

Type: article

Abstract: Accurate delineating individual agricultural fields (AFs) plays a key role in advancing precision agriculture and effective land management. Although deep learning techniques have shown great potential in automatically extracting AFs from satellite imagery, existing models still face significant challenges, including field adhesion, inaccurate boundary localization, and limited transferability. To address this, we developed a novel refined boundary-semantic fusion multitask learning framework (RBSF-Net) that integrates precisely located shallow boundary features with deep semantic features to extract fine-scale AFs from high-resolution satellite imagery. First, a dual-branch hybrid neural network was used to capture the boundary and semantic features of AFs, respectively. Second, a boundary patch refinement module was designed to postprocess fragmented boundaries by utilizing directional information. Finally, a boundary-semantic interaction module, based on a marker-controlled watershed fusion strategy, was employed to effectively integrate boundary-semantic features, generating the final individual AF entities by using deep semantic features as markers and shallow boundary features as guide maps. The proposed RBSF-Net achieved significant improvements in AFs extraction compared to current state-of-the-art methods (i.e., HDNet, BSiNet, E2EVAP, Deeplabv3+, ResU-Net, and U-Net), with an accuracy of$F{{1}_{\text{extent}}}$at 0.880,$F{{1}_{\text{edge}}}$at 0.919, and${{S}_{\text{total}}}$at 0.169. The RBSF-Net also achieved a satisfactory accuracy across three diverse regions with different imagery resolutions, diverse agricultural landscapes, and different terrain conditions, performing great generalization and robustness. These findings suggest that RBSF-Net, which is an accurate, robust, and generalized method, holds substantial potential for broad application in precise large-scale AFs identification.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI: 10.1109/jstars.2025.3590399

Authors: Mengyu Liu, Ziming Wang, Jiaqi Chen, Chen Wang, Cheng Cai, Huanxue Zhang

Institution: Shandong Normal University

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $1,800