Change Detection Method of Remote Sensing Image Based on Steerable Kernel Regression and Superpixel Segmentation

Abstract

The invention discloses a remote sensing image change detection method based on controllable kernel regression and superpixel segmentation. The problems that only grey information of an image is considered when a difference chart is structured, other feature information is underused, k-means clustering is directly carried out on the difference chart, and therefore the situation that a weak declension area cannot be detected is easily caused are mainly solved. The method comprises the steps of adopting the controllable kernel regression on two input time phase images to respectively extract structural feature matrixes, combining feature matrixes of neighbourhoods with the structural feature matrixes respectively, obtaining a local structural feature matrix, decomposing the local structural feature matrix by using a non-negative matrix factorization algorithm, carrying out a difference chart structure on an obtained coefficient matrix, finally segmenting the difference chart to obtain an over-segmentation image by using a superpixel segmentation method, carrying out the K-means clustering on the over-segmentation image, and obtaining a change detection result. The remote sensing image change detection method based on the controllable kernel regression and the superpixel segmentation can keep marginal information of images, is good in noise proof performance, improves change detection precision, and can be applied to fields of disaster situation monitoring, land utilization, agricultural investigation and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to remote sensing image change detection, in particular to a remote sensing image change detection method based on controllable kernel regression SKR and superpixel segmentation, which can be used to detect remote sensing image change regions. Background technique [0002] Remote sensing image change detection is to compare and analyze remote sensing images acquired in different time phases in the same area to obtain the change area of ​​the two time phases. Remote sensing image change detection has been widely used in many industries, such as: monitoring of changes in water resources, land, forests, pastures and other resources; monitoring of changes in oceans, lakes, wetlands, urban areas, etc.; Disaster assessment, crop growth status assessment, earth data correction and update, etc. In addition, remote sensing image change detection has important military applications, suc...

AI Technical Summary

Problems solved by technology

This method uses graph-cut and FCM for initial segmentation, considering the local characteristics of the image. The initial segmentation result better reflects the actual situation of the image and improves the accuracy of the final segmentation result. However, this method directly segments the difference map. , which makes the method sensitive to noise, and misses detection in weakly changing areas, resulting in low accuracy of final detection results

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