Slope geological disaster extraction method based on multi-feature auxiliary watershed algorithm

Abstract

The invention belongs to the field of geological disaster information extraction, and discloses a slope geological disaster extraction method based on a multi-feature auxiliary watershed algorithm, which comprises the following steps: segmenting an image to be segmented through a simulated immersion watershed algorithm, and converting the segmented region from an RGB color space to an Luv color space; searching for a minimum region in all the segmented regions; traversing all the adjacent regions of each minimum region, and combining the adjacent regions of which the chromatic aberration values meet conditions with the minimum regions until all the minimum regions are combined; calculating each characteristic factor image layer and superposing the characteristic factor image layers with the merged segmentation area; and setting a topographic feature threshold, a sensitivity feature threshold and a geometrical feature threshold, and removing regions which do not meet threshold conditions, so that the remaining regions are the extracted slope geological disaster pattern spots. The slope geological disaster time efficiency and accuracy of the extracted experimental area are high, theslope disaster boundary and the visual interpretation result are good in goodness of fit, manual intervention in the extraction process is little, and objectivity is high.

Description

Technical field [0001] The invention belongs to the field of geological disaster information extraction, and specifically relates to a method for extracting slope geological disasters with a multi-feature auxiliary watershed algorithm. Background technique [0002] The extraction of slope geological hazard information is one of the important research contents of remote sensing technology in the field of geological hazards, and it is of great significance to quickly grasp the development status and potential risks of regional slope geological hazards. With the rapid development of remote sensing image information extraction technology, the traditional pixel-based classification technology is gradually converted to object-oriented segmentation, and some researches are based on support vector machines, machine learning, and deep learning. At present, many researchers have completed the object-oriented slope disaster extraction research through multi-scale and multi-feature combinati...

AI Technical Summary

Problems solved by technology

[0004] To sum up, it is difficult to accurately extract slope geological hazards from remote sensing images in the existing technology, mainly in the following two aspects: 1) the existing methods for remote sensing image segmentation and extraction of slope geological hazards are immature; 2) slope geology Many disaster-pregnant factors of disasters failed to participate in the extraction, resulting in limited extraction results

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