Research On Remote Sensing Image Change Detection And Its Application

Remote sensing image change detection refers to the technology of data analysis of two or more remote sensing images acquired at the same geographical position at different phases to obtain the true change information of the surface target.Remote sensing image change detection technology is widely used,such as dynamic changes in forest or vegetation monitoring,changes in land use and cover analysis,floods,fire and other natural disasters after the disaster analysis and assessment of farmland monitoring,analysis of crop growth,real-time monitoring of urban changes(such as streets,buildings,etc.),and dynamic monitoring of military strategic objectives(such as airports and roads).In this paper,the SAR images are firstly researched and processed,and then the proposed algorithms are applied to other remote sensing images.Finally,a small system is designed based on the graphical user interface GUI provided by MATLAB,which can be accurate efficient detection of changes in the target area.The main work is as follows:(1)Traditional image change detection based on nonsubsampled contourlet transform always ignores the neighborhood information's relationship to the nonsubsampled contourlet coefficients,and the detection results are susceptible to noise interference.To address these disadvantages,we propose a denoising method based on the nonsubsampled contourlet transform domain that uses the Hidden Markov Tree model(NSCT-HMT)for change detection of remote sensing images.First,the ENVI software is used to calibrate the original remote sensing images.After that,the mean-ratio operation is adopted to obtain the difference image that will be denoised by the NSCT-HMT model.Then,using the Fuzzy Local Information C-means(FLICM)algorithm,the difference image is divided into the change area and unchanged area.The proposed algorithm is applied to a real remote sensing data set.The application results show that the proposed algorithm can effectively suppress clutter noise,and retain more detailed information from the original images.Finally,the algorithm is compared with the other four algorithms to verify the superiority of the proposed algorithm.(2)In order to improve the accuracy of synthetic aperture radar(SAR)image change detection and have good change detection results,we proposes a method based on non-subsampled shearlet transform(NSST)detection in SAR images for unsupervised changes.First,two original registered images go through smoothing filtering,and then the normalized difference ratio method is used to obtain the difference image.After that,NSST is used to decompose the distance map into low frequency and high frequency sub-bands.The low frequency sub-bands are processed using linear enhancement,and the high frequency sub-bands are processed using the adaptive threshold method.Then,inverse NSST is used to obtain the enhanced difference figure.Finally,the fuzzy local information C clustering(FLICM)algorithm is used for clustering the pixels of the image into changed and unchanged sections.The experimental results show that the algorithm can effectively improve the accuracy of remote sensing image change detection,and it is not affected by the statistical distribution of the changed and unchanged sections.(3)Based on the graphical user interface(GUI)provided by MATLAB,a small image change detection system is designed and implemented.The system can select the area,time limit and algorithm to be processed according to the user's needs,and can detect the difference between different time zones in real time.Finally,taking the eight prefectures in Xinjiang as an example,it shows that the system can achieve good results when applied to the change of real remote sensing images.