Automatic change detection method for remote sensing image in large-scale complex scene

Abstract

The invention relates to an automatic change detection method for a remote sensing image in a large-scale complex scene. The method comprises the following steps: S1, acquiring a remote sensing imagedata pair of front and back time phases; S2, extracting feature points from the remote sensing image data pair, and performing image registration; S3, obtaining a difference image through a differencemethod based on the registered remote sensing image data pair; S4, extracting the saliency of the difference image, and generating a variable pseudo training sample and an invariable pseudo trainingsample; and S5, inputting the variable pseudo training sample and the invariable pseudo training sample into a classifier, performing binary classification on the difference image obtained in step S3,and obtaining binary detection results about the variable type and the invariable type. Compared with the prior art, the method can be applied to change detection of remote sensing images in large-scale complex scenes, and has the advantages of high detection and recognition precision, high efficiency and the like.

Description

technical field [0001] The invention relates to the field of automatic detection of multi-temporal remote sensing images, in particular to an automatic change detection method for remote sensing images in large-scale and complex scenes. Background technique [0002] Change detection is the use of remote sensing images in different periods to quantitatively analyze and determine the characteristics and process of ground object changes. In recent years, with the accelerated change of the land surface, it has become an increasingly urgent task to accurately and automatically identify land cover changes in multi-temporal remote sensing images. In the past few decades, scholars from various countries have continuously proposed many novel change detection technologies. Some more advanced change detection technologies can achieve robust detection on the fine scale of remote sensing images, and have been effectively used in different remote sensing applications such as agriculture. ...

AI Technical Summary

Problems solved by technology

On the other hand, the design of traditional change detection methods is highly dependent on the accurate multi-temporal remote sensing image registration process

This process is usually performed manually and registration residuals can lead to misalignment of the same land cover causing false detection of unrelated changes

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