关键词: 图形提示, 遥感影像, 深度学习, 交互式分割, 地物提取

Abstract:

High-resolution remote sensing image object extraction is a critical technology supporting key areas such as smart city development and natural resource monitoring. However, existing fully automatic methods still face dual challenges in practical applications—limited model adaptability and high manual annotation costs. To address these issues, this paper proposes a high-precision object extraction framework for remote sensing imagery based on minimal interactions (e.g., points, strokes, boxes). By systematically analyzing the limitations of current interactive segmentation techniques, we innovatively construct a unified extraction framework that integrates precise interactive segmentation and batch identical-object detection. The framework comprises two core algorithms: ①A one-shot precision extraction algorithm based on fine-tuning strategies, enabling high-quality object segmentation under minimal interaction; ②A rapid detection algorithm for identical objects, which leverages existing segmentation masks to achieve efficient batch annotation of identical objects. In addition, the extraction framework includes empirical post-processing of geospatial extraction results to obtain vector extraction results. Experimental results on typical facet objects such as buildings, water bodies, and forested areas demonstrate that the proposed method significantly reduces user interactions while maintaining high segmentation accuracy. It outperforms state-of-the-art general-purpose segmentation models, such as segment anything model (SAM) and EISeg. This study provides an innovative solution for efficient annotation of remote sensing image samples and offers significant potential for advancing the automation and practical utility of intelligent remote sensing interpretation.

Key words: graphical prompts, remote sensing imagery, deep learning, interactive segmentation, object extraction