Research On Change Detection In Remote Sensing Imagery

Change Detection in remote sensing imagery is defined as the procedure of quantitatively analyzing and identifying changes occurred on the earth' s surface from remote sensing imageries acquired at different times. As a key element for many applications of erath observation such as resource inventory, environment monitoring, update of fundamental geographical database, etc,change detection technique is of urgent demands and has great potentiall in scientific applications. So far ,many scients have developed many methods for change detection , and these methods can be classified as superviseed and unsupervised techniques. The former require the availability of a "ground truth" from which to derive a training set containing information about the spectral signatures of changes. The latter performs change detection without any additional information besides the raw images considered. It is obvious that using unsupervised techniques is mandateory in many remote-sensing applications, as suitable ground-truth information is not always available. The change-detection processs performed by such unsupervised techniques is usually divided into three main sequentiall steps:1)pre-precessing, aimed at rendering the two images comparable in both the spatial and spectral domains. 2) image comparison and 3)analysis of the difference image.This paper, we focus on the last step of change-detection process. We have finished three main tasks about the change region extract:Firstly, a method base on Bayes decion for the minimum error is proposed to estabish change threshold. Indor to overcome the diffiult of establish change thresholds in difference image, this paper summarizes the original methods in establishing threshold, and then proposed the Bayes decision for minimum error to establish the threshold which used the Expectation-Maximization algorithm, and compared the difference when the image subject to Gauss and General Gauss Model.Secondly, The top method considered the pixels are all independence and the contextual information is ignored, the result was not good enough. The contextual method is devised for this problem. In this method, probability looseness and markov random field model are used in changedetection .the results improved the accuracy and reliability of the changed area extraction.Thirdly, we proposed line change detection in liner object. Because the above methods are not all consider the line feature of the line object, the results are not so good. We propose a method that considers the edge and gray information to detect the changes, and the results improve that the method is so good.Experimental results demonstrate that the algorithm proprosed in this paper is effective for indentifying and extracting changed areas from difference images.