Multi-spectrum remote sensing image change detection method

Abstract

The invention discloses a multi-spectrum remote sensing image change detection method. The traditional remote sensing image change detection method comprises the following steps of forming a difference image on the two remote sensing images different in time phases through an algebraic method, then modeling the difference image, determining the change threshold value, and detecting the change of the image. However, the process has the problems that the difference image may not meet the designated model and the change threshold value is difficultly determined, so the remote sensing image change detection method based on an SVM (support vector machine) mixed kernel function is put forward for solving the problems. The implementation process comprises the following steps of firstly, constructing the difference image in a way of combining PCA (primary component analysis) conversion and a correlation coefficient integrating method; then, extracting the gray feature and texture feature of the difference image, and carrying out normalization and formatting; selecting a training area, and constructing the SVM mixed kernel function to train; finally, enabling an SVM mixed kernel classifier to classify the difference image, so the change detection result of the target image is obtained.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, in particular to a multi-spectral remote sensing image change detection method, which is suitable for change detection of multiple multi-spectral remote sensing image data containing ground object change information in different time phases in the same area. Background technique [0002] Change detection based on remote sensing images is to identify the state change process of observed objects or phenomena based on remote sensing images of different time phases in the same area. It has been widely used in resource management and planning, environmental protection and many other fields, and provides scientific decision-making basis for relevant departments. [0003] At present, the problem of change detection in remote sensing images has become a very active research direction, such as the National Institute of Geospatial Intelligence in the United States, the University of...

AI Technical Summary

Problems solved by technology

However, when the "difference image" contains multiple types of ground object changes and the spectral information is complex, treating the difference image as a Gaussian mixture distribution with only two types will obviously reduce its change detection accuracy

In addition, the EM algorithm estimates the parameters when the number of branches of the Gaussian Mixture Model (GMM) is known, but in practical applications, this is difficult to achieve

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