Method and device for establishing and identifying landslide classification model in complex background area

Abstract

The invention provides a complex background area landslide classification model establishment method and device and a complex background area landslide identification method and device, and relates toclass imbalance model establishment and landslide identification. The method for establishing the landslide classification model in the complex background area comprises the following steps: acquiring laser radar data of a research area; constructing a terrain object according to the laser radar data, and determining a terrain object feature vector according to the terrain object so as to determine a data set; performing joint optimization on classification model parameters and balance coefficients according to the data set to determine a collaborative optimal balance coefficient and collaborative optimal classification model parameters; determining a robust sensitive feature subset after class balance according to the collaborative optimal balance coefficient and the collaborative optimal classification model parameter; and establishing a landslide classification model according to the robust sensitive feature subset after class balance. According to the technical scheme, the landslide remote sensing recognition precision is improved.

Description

technical field [0001] The invention relates to the technical field of class imbalance model establishment and landslide identification, in particular to a landslide classification model establishment and identification method and device in complex background areas. Background technique [0002] In the prior art, in the field of landslide remote sensing recognition, the problem of class imbalance has not attracted enough attention. There are two problems in the learning method of remote sensing class imbalance: (1) lack of an effective method for optimizing the balance coefficient, resulting in the inability to obtain the optimal (2) The lack of a robust and sensitive feature subset after class balancing cannot provide an optimized data set for subsequent classification. These problems further restrict the improvement of landslide remote sensing identification accuracy. Contents of the invention [0003] The problem solved by the invention is how to improve the recognitio...

AI Technical Summary

Problems solved by technology

[0002] In the prior art, in the field of landslide remote sensing recognition, the problem of class imbalance has not attracted enough attention. There are two problems in the learning method of remote sensing class imbalance: (1) lack of an effective method for optimizing the balance coefficient, resulting in the inability to obtain the optimal (2) Lack of robust sensitive feature subsets after class balance, unable to provide optimized data sets for subsequent classification

These problems further restrict the improvement of landslide remote sensing recognition accuracy.

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