Method for detecting remote sensing image change based on adaptive difference images

Abstract

The invention discloses a method for detecting remote sensing image change based on adaptive Treelet construction difference images, belongs to the field of image processing technology and mainly assists in solving a problem of change detection precision deficiency in the prior art. The method is realized by carrying out block processing of two inputted different-phase remote sensing images, namely calculating difference values between an image block in a search window of a second image and a center image block of a first image to obtain sample matrixes; clustering the sample matrixes by using Treelet algorithm to obtain adaptive difference images; calculating Otsu thresholds of differential-value difference images and adaptive difference images; integrating the differential-value difference images and the adaptive difference images by using the thresholds to obtain final difference images, and carrying out Otsu threshold segmentation on the final difference images to obtain change detection results. According to the method in the invention, change detection precision can be improved effectively; image edge information can be maintained; and the method can be used for detecting disaster situation and in land utilization.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, in particular to a remote sensing image change detection method based on an adaptive Treelet structure difference map, which is suitable for digital image processing in environment and urban planning. Background technique [0002] The change detection of remote sensing images refers to the analysis of remote sensing images of the same geographical location in different periods to obtain the change information, which is the main development direction of the current remote sensing data processing technology. [0003] The change detection method of two remote sensing images after registration is usually to obtain the difference image through direct operation, and then analyze and process the difference image to obtain the detection result map of the classification of change and non-change. This method is relatively simple, direct, and easy to implement. The disadvantage is that the ...

AI Technical Summary

Problems solved by technology

This method is relatively simple, direct, and easy to implement. The disadvantage is that the obtained difference image requires high preprocessing of remote sensing image data, and the noise in the difference image has a great impact on the detection results.

[0004] In order to improve the detection effect of direct difference images and improve the accuracy of change detection, scholars such as Lingcao Huang proposed a weighted fusion Sobel edge texture map and difference difference map in the article "An Object-based Change Detection Approach by Integrating Intensity and Texture Differences" The probability difference map is obtained, and the threshold is used to segment the fused probability difference map to obtain a change detection image. While maintaining the change area, a large number of noise points are introduced, which affects the result of change detection.

Wei He proposed in the article "Application of Euclidean norm in Multi-temporal Remote Sensing Image Change Detection" to use the Euclidean distance norm of the difference between two remote sensing images to represent the difference map, and then use the maximum entropy threshold method to extract the change part. This method suppresses noise information, but does not maintain details such as edges well, reducing the visual effect of changing areas

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