Landslide mass recognition method based on Laplacian pyramid remote sensing image fusion

Abstract

The invention discloses a landslide mass recognition method based on Laplacian pyramid remote sensing image fusion. The method comprises the steps: reconstructing an original remote sensing image through a Laplacian pyramid fusion module according to the extracted local features and global features of the remote sensing image, and generating a fusion image; constructing a deep learning semantic segmentation model through a semantic segmentation network; then respectively marking places where landslide disasters occur and places where landslide disasters do not occur in the fused image through a picture marking tool to obtain a landslide disaster tag map data set; and finally, training a deep learning semantic segmentation model by using the data set, and storing the model by modifying a semantic segmentation network structure and adjusting model parameters until a loss curve of the model reaches fitting and when the precision of recognizing the landslide mass in the remote sensing image meets the requirement. According to the method, an image fusion model based on the Laplacian pyramid is combined, and an effective decision basis can be efficiently and accurately provided for disaster prevention and reduction of landslide disasters.

Description

technical field [0001] The invention relates to a landslide body recognition method, in particular to a landslide body recognition method based on Laplacian pyramid remote sensing image fusion. Background technique [0002] As one of the most dangerous natural disasters, landslides are generally defined as natural phenomena in which soil or rock on a slope slides down the slope under the action of gravity under the influence of factors such as river erosion and earthquakes. It often occurs in mountainous areas. , hills and other areas. Landslide disasters are highly destructive and pose a huge threat to the ecological environment, transportation, and construction land. The resulting casualties and property losses are huge. Therefore, it is necessary to monitor landslides in real time to reduce losses. It is difficult to systematically identify landslides because of their unpredictable occurrence, scattered distribution and complex topography in the disaster area. In recent...

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Problems solved by technology

[0003] However, the above-mentioned technologies still have the following shortcomings: 1. The accuracy of the traditional remote sensing landslide identification method is not high, it is difficult to exceed 90%, and it is limited by factors such as available data; 2. Due to the different observation dimensions of multi-source remote sensing, the time of image , Spatial and spectral resolutions are different, which makes the information redundant and cannot make full use of the advantages of multi-source data, and the algorithm based on image fusion generally has serious spatial and spectral distortion problems

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