Remote sensing image change detection method based on controllable 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] 本发明属于图像处理技术领域,涉及遥感图像变化检测,具体地说是一种基于可控核回归SKR和超像素分割的遥感图像变化检测方法,可用于对遥感图像变化区域的检测。 Background technique [0002] 遥感图像变化检测是对同一地区,不同时相获取的遥感影像进行对比、分析,得到两时相的变化区域。遥感图像变化检测在诸多行业中已经具有广泛应用,例如:水资源、土地、森林、草场等资源变化监测,海洋、湖泊、湿地、城区等的变化监测,海啸、地震、火灾、农作物病虫害等自然灾害评估,农作物生长状况评估,地球数据校正更新等方面;除此以外,遥感图像变化检测在军事方面具有重要应用,例如战场情报获取、军事目标侦查、军力部署情况调查等。随着遥感图像变化检测应用的日趋广泛,遥感图像变化检测方法的研究也成为国内外学者研究的重要方面。 [0003] 首先对同一地区不同时相两幅遥感图像进行对比构造差异图,再对构造的差异图进行分类从而找到变化区域的先比较后分类方法是遥感图像变化检测的一类重要方法。一般的先分类后比较方法,对两幅图像进行直接相减构造差异图,再对构造的差异图利用分割、聚类方法进行分类。然而此类方法一般只使用图像灰度信息,没有考虑图像的其他信息,从而造成检测结果对噪声敏感,对弱变化区域漏检高等问题,同时,此类方法在对差异图进行聚类的时候,直接采用k-means聚类,容易造成弱变化区域难以检测的问题。 [0004] 许多学者对先比较后分类的方法,进行了深入的研究。Wang等在文章“Unsupervised Change Detection for Remote Rensing Images Using multiscale Decomposition and Treelet Fusion:A Level Set Approach,Proceedings of2011IEEE CIE International Conference on Radar(RADAR2011),2011:1558-1561.”中提出了基于Treelet融合和水平集分割的变化检测方法。该方法对两个时相的图像分别进行小波分解,对分解后的对应子代系数进行做差,再对具有边缘信息的垂直和水平子代系数差进行soble算子边缘增强,然后进行重构和tr...

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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