A Landslide Recognition Method Based on Laplacian Pyramid Remote Sensing Image Fusion

Abstract

The invention discloses a landslide body identification method based on Laplacian pyramid remote sensing image fusion, which comprises the steps of: first reconstructing the original remote sensing image through the Laplacian pyramid fusion module for the extracted local features and global features of the remote sensing image, and generating Fusion images; then construct a deep learning semantic segmentation model through the semantic segmentation network; then use the image labeling tool to mark the places where landslide disasters occurred and the places where landslide disasters did not occur in the fused image, and obtain a landslide disaster label map dataset; Finally, the dataset is used to train the deep learning semantic segmentation model. By modifying the semantic segmentation network structure and adjusting the model parameters, the model is saved until the loss curve of the model is fitted and the accuracy of identifying landslides in remote sensing images meets the requirements. The invention combines the image fusion model based on the Laplacian pyramid, and can efficiently and accurately provide effective decision-making basis for disaster prevention and mitigation 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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