Remote sensing image change detection method based on area and Kmeans clustering

Abstract

The invention discloses a remote sensing image change detection method based on an area and Kmeans clustering. The remote sensing image change detection method based on the area and the Kmeans clustering mainly solves the problems that an existing detection result has isolated pixel points and cavities exist in the area. The remote sensing image change detection method based on the area and the Kmeans clustering comprises the steps of reading-in two registered images X1 and X2, wherein the time phase of the image X1 is different from the time phase of the image X2; constructing a difference image after the image X1 and the image X2 are filtered; carrying out maximum entropy threshold segmentation on the difference image to extract interested areas and confirm non-variable areas; calculating feature vectors of the interested areas and the non-variable areas; dividing all areas into two classes according to the characteristics of the confirmed non-variable areas and all the interested areas by means of the Kmeans clustering algorithm; using an area corresponding to a difference image with a high graying value as a variable area and other areas of the difference image are used as non-variable areas according to a clustering result, and finally obtaining a change detection result. According to the remote sensing image change detection method based on the area and the Kmeans clustering, the detection result can maintain the consistency of the interior of the area, the isolated pixel points are removed, and detection precision is improved. The remote sensing image change detection method based on the area and the Kmeans clustering can be used for resource monitoring and disaster evaluation.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to change detection of optical remote sensing images, in particular to a method for detecting changes of remote sensing images based on region and Kmeans clustering, which is suitable for remote sensing image processing and analysis. Background technique [0002] Remote sensing image change detection is to extract the area of ​​change in two time-phase remote sensing images, which has been widely used in many fields of economy and society, such as land resource monitoring, water body monitoring, environmental monitoring, forestry monitoring, agricultural survey, vegetation coverage and meteorological Surveillance, disaster monitoring and assessment, urban management planning, and even military reconnaissance and battlefield estimation. [0003] In practical applications, such as changes in land cover, land degradation and desertification, changes in rivers and lakes, changes i...

AI Technical Summary

Problems solved by technology

This method can suppress the generation of false change information, and make the location of the change area more accurate through correction, but this method cannot keep the edge of the change area well due to many missed detections.

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