High-resolution image landslide automatic detection method based on multi-level perception feature progressive self-learning

Abstract

The invention relates to a high-resolution image landslide automatic detection method based on multi-level perception feature progressive self-learning, and the method comprises the steps: 1, dividingfeature levels based on perception depth through employing a high-resolution image of a landslide region in order to support the feature normalization mapping of perception level progressive; 2, establishing a scale normalization model with progressively enhanced features, mapping perception feature elements of each level by taking spatial scales and dimensions as carriers, and generating a multi-level feature map with highly organized semantic information; 3, constructing a progressive self-learning regionalized network integrated with multi-level feature map constraints, and generating a landslide target-oriented detection network through end-to-end training; and d) inputting to-be-analyzed target high-resolution image data to the detection network, performing targeted detection of thelandslide target from the perspective of gradually enhancing image features, and finally outputting landslide target image representation. The method overcomes the defect of single understanding of complex scene images in the prior art, enhances the association ability between the features, and enables the detection result to be more accurate.

Description

technical field [0001] The invention belongs to the technical field of geospatial data processing, and in particular relates to a high-resolution image landslide automatic detection method for progressive self-learning of multi-level perceptual features. Background technique [0002] Due to the widespread, lossy, and threatening effects of landslide hazards, effective and rapid identification of them has become an urgent problem to be solved today. Field surveys can no longer meet the needs of landslide disaster identification, and identification using optical satellite remote sensing technology has become the mainstream. Among them, target recognition based on object-oriented classification method can extract key information and target area, combined with visual interpretation, can obtain detailed information of landslides, and improve the success rate of remote sensing image landslide interpretation. [0003] The research results show that using the traditional object-ori...

AI Technical Summary

Problems solved by technology

[0007] While analyzing and mining remote sensing data, we are faced with the problem that a large number of non-standard features cannot be effectively used, so that the machine cannot accurately learn image features, and cannot give accurate judgments for massive data to be predicted, which is not universal. Creates a situation where the resulting features are meaningless or learning stalls

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