Multiscale Edge-Guided Network for Accurate Cultivated Land Parcel Boundary Extraction From Remote Sensing Images

The accurate acquisition of farmland information holds paramount importance for effective agricultural resource monitoring and production management. Traditional semantic segmentation methods struggle to perform precise segmentation at the parcel level. Many existing contour extraction methods tend to generate ambiguous and inaccurate outcomes. To overcome these challenges, this article proposes a multiscale edge-guided network for accurate cultivated land parcel boundary extraction, which consists of the following: 1) multiscale guided transformer module: this module is designed to encode parcel features by combining the shunted transformer and atrous convolution modules; it allows for modeling long-distance context and refining features, enabling accurate representation of farmland parcels; 2) edge enhancement module: this module operates on multiscale features during the feature extraction stage, improving the ability to capture fine details and boundaries of farmland parcels; 3) dual-pyramid structure: this structure consists of a bottom-up pyramid that incorporates deformable convolutions; it enhances the accuracy of multiscale object detection, enabling the network to capture features at different levels of detail; and 4) deoverlap operation: this modular is designed to reduce the ambiguity caused by contour overlap in the extracted results. Specifically, the method effectively mitigates the impact of regional, temporal, and background features. The experimental results showcase the attainment of average precision (AP) and mean intersection over union (MIoU) scores of 0.3421 and 85.28, respectively, for the extracted farmland parcels. Moreover, the method yields competitive results across different farmland parcel types, shapes, and temporal intervals. This positions it as a valuable tool for optimizing agricultural production and enhancing resource monitoring capabilities.