Cross-scale building extraction method based on pseudo-label refinement and related device

CN122435429BActive Publication Date: 2026-10-16GUANGZHOU UNIVERSITY
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Patent Information

Application Number
CN202610305793.0
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-13
Publication Date
2026-10-16
Estimated Expiration
2046-03-13

AI Technical Summary

Technical Problem

然而该类方法仍然仅从语义概率层面抑制噪声，并未从几何结构层面对建筑物边界进行修正

Benefits of technology

[0015] In this embodiment of the invention, a segmentation network is trained based on labeled source domain data; initial pseudo-labels are generated using unlabeled target domain images based on the trained segmentation network; adaptive weight adjustment of uncertain regions is performed on the initial pseudo-labels based on dual prediction branches; geometric correction is performed on the initial pseudo-labels based on overlap rate determination; refined pseudo-labels are generated based on the geometric correction results and adaptive weight adjustment results; the trained segmentation network is iteratively self-trained based on the refined pseudo-labels; and cross-scale building extraction is performed on the target remote sensing images based on the iteratively self-trained segmentation network. This achieves synergistic optimization of semantic probability information and geometric structure information, effectively improving the model's building extraction accuracy and cross-domain adaptability in cross-platform and cross-resolution remote sensing images under unlabeled target domain conditions.

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Abstract

The application discloses a cross-scale building extraction method based on pseudo-label refining and a related device, and relates to the technical field of machine learning. The method comprises the following steps: training a segmentation network based on labeled source domain data; generating initial pseudo-labels by using unlabeled target domain images based on the trained segmentation network; adaptively adjusting the weights of uncertain regions of the initial pseudo-labels based on a double-prediction branch; geometrically correcting the initial pseudo-labels based on an overlap rate judgment, generating refined pseudo-labels based on the geometric correction result and the adaptive weight adjustment result; iteratively training the trained segmentation network based on the refined pseudo-labels, extracting cross-scale buildings from target remote sensing images based on the segmentation network after the iterative training, and obtaining a cross-scale building extraction result. The application can effectively improve the building extraction precision and cross-domain adaptability of the model in cross-platform and cross-resolution remote sensing images under the condition of no target domain labeling.
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