Remote sensing image ground fracture identification method, device, equipment, medium and product

By combining the improved U-Net network with ResNet50 and attention mechanism, the problems of low efficiency and low accuracy of traditional ground fissure identification methods are solved, and efficient and accurate identification and extraction of ground fissures are achieved.

CN121789079APending Publication Date: 2026-04-03TAIYUAN UNIVERSITY OF TECHNOLOGY
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Patent Information

Application Number
CN202610101560.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-01-26
Publication Date
2026-04-03

AI Technical Summary

Technical Problem

Traditional methods for identifying ground fissures are inefficient and inaccurate, especially in complex environments where they are sensitive to noise and lighting changes, making it difficult to achieve efficient and accurate identification.

Method used

An improved U-Net network is used for ground fissure identification. ResNet50 is used as the backbone network to extract deep features, and an attention mechanism is introduced in the decoding stage. By upsampling and convolution of multi-scale deep feature maps and semantic features, combined with the attention module, the ability to identify fissure regions is improved.

Benefits of technology

It enables accurate identification and continuous extraction of ground fissures of different scales and morphologies, improving identification efficiency and accuracy. It can effectively suppress background interference in complex backgrounds and enhance the ability to express the features of the fissure region.

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Abstract

The invention discloses a remote sensing image ground fracture recognition method and device, equipment, a medium and a product, and relates to the field of computer vision and remote sensing image processing.The method comprises the steps that an actual satellite remote sensing image of a to-be-recognized area is obtained; identifying the actual satellite remote sensing image by using the ground fracture identification network model to obtain a ground fracture identification result; the ground fracture identification network model is a trained improved U-Net network; the improved U-Net network uses ResNet50 as a skeleton network to extract deep features of data in a coding stage, and introduces an attention mechanism in a decoding stage to improve the extraction effect of earth surface cracks. According to the invention, the ground fracture identification efficiency and accuracy are improved.
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