Remote sensing image change detection method based on controllable kernel regression and superpixel segmentation
What is Al technical title?
Al technical title is built by PatSnap Al team. It summarizes the technical point description of the patent document.
A superpixel segmentation and remote sensing image technology, applied in the field of image processing, can solve the problems of noise sensitivity, missed detection, and low accuracy of final detection results, and achieve the effect of improving the accuracy rate, reducing the total number of errors, and suppressing noise.
Inactive Publication Date: 2013-08-21
XIDIAN UNIV
View PDF3 Cites 19 Cited by
Summary
Abstract
Description
Claims
Application Information
AI Technical Summary
This helps you quickly interpret patents by identifying the three key elements:
Problems solved by technology
Method used
Benefits of technology
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
Method used
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more
Image
Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
Click on the blue label to locate the original text in one second.
Reading with bidirectional positioning of images and text.
Smart Image
Examples
Experimental program
Comparison scheme
Effect test
Embodiment Construction
[0024] refer to figure 1 ,本发明的具体实现步骤如下:
[0025] 步骤1,输入同一地区不同时间获取的已配准的两幅遥感图像X 1 and x 2 .
[0026] 输入同一地区获取的已配准的两幅大小均为I×J的两时相遥感图像:第一时相图像为X 1 ={X 1 (i,j)|1≤i≤I,1≤j≤J},第二时相图像为X 2 ={X 2(i,j)|1≤i≤I,1≤j≤J}, where X 1 (i,j) and X 2 (i, j) are the pixel gray value of the first phase image and the second phase image at the spatial position (i, j) respectively, i and j are the row number and column number of the image respectively, and I is the image The total number of rows, J is the total number of columns of the image.
[0027] Step 2, calculate the first phase image X 1 and the second phase image X 2 The structural feature matrix W S1 and W S2 .
[0028] The methods for calculating the structural feature matrix include nonlinear adaptive interpolation method, anisotropic directional diffusion PDE method, controllable kernel regression method, etc. The controllable kernel regression method given in this embodiment has the following specific steps:
[0029] 2a) The fir...
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more
PUM
Login to view more
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...
Claims
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more
Application Information
Patent Timeline
Application Date:The date an application was filed.
Publication Date:The date a patent or application was officially published.
First Publication Date:The earliest publication date of a patent with the same application number.
Issue Date:Publication date of the patent grant document.
PCT Entry Date:The Entry date of PCT National Phase.
Estimated Expiry Date:The statutory expiry date of a patent right according to the Patent Law, and it is the longest term of protection that the patent right can achieve without the termination of the patent right due to other reasons(Term extension factor has been taken into account ).
Invalid Date:Actual expiry date is based on effective date or publication date of legal transaction data of invalid patent.