Research On Change Detection Method And Application Based On Remote Sensing Image

As the aerospace technology develops,remote sensing image processing technology has been used by all walks of life in society.Research on the remote sensing image change detection serves as an extremely important research direction in the field of remote sensing image processing,which has been widely applied in these fields,such as land resource management,military situation monitoring and natural disaster monitoring.In the late 20 th century,satellite technology entered the fast path of development,thus remote sensing image data broke the bottleneck of data acquisition,got accumulated and greatly pushed the field of research on the remote sensing image change detection forward.However,the accuracy of remote sensing image change detection is related to satellite sensors,shooting seasons and climate environment,so these factors make the change detection more difficult.Therefore,it has an important research significance to improve the accuracy of remote sensing image change detection.The article analyzes the papers on change detection in recent years,and concludes the current research status in this area at home and abroad.The article selects dual-temporal multi-spectral and SAR remote sensing images as the research objects for research.The main work includes the following aspects.(1)Developed change detection on remote sensing image based on pixel characteristics.First,It finished the change detection experiment which is based on difference method,ratio method and log ratio method.The results showed that the log ratio method has higher changes in processing SAR images detection accuracy.Then the change detection research about the texture feature difference map is carried out,on the basis of this research.A feature difference map of gray level co-occurrence matrix,and a Gabor texture feature difference map.Finally,threshold segmentation and Fuzzy C-Means(Fuzzy C-Means,FCM)methods are used to extract the change area.By comparing the accuracy evaluation results of different methods.And analyzing the main factors which affects the accuracy of change detection.(2)Using object-oriented processing ideas to carry out remote sensing image change detection,the first step is to segment the remote sensing image into image objects,according to certain segmentation criteria.Then extract the features of the image objects and construct the difference map based on the object treat.At last,divide the change area.The segmentation accuracy of image objects determines the accuracy of object-oriented remote sensing image change detection.This paper makes a comparition of object-oriented change detection methods based on superpixel segmentation and mean-shift clustering as well as the accuracy of change detection under different methods and segmentation scales,and analyze the main factors which affects its accuracy.Compare the accuracy of change detection under different methods and different segmentation scales,and analyze the main factors affecting its accuracy.(3)The problem of change detection based on remote sensing images can be transformed into a problem of pixel-level classification of difference images.This article draws on the design ideas of Fully Convolution Networks(FCN),and improves on the basis of the U-net semantic segmentation network.Design a two-class neural network model suitable for remote sensing images.First,perform operations such as labeling,segmentation and bit changes to make a dataset.Then input it into the designed U-net model for parameter learning.Experimental results show that this model has high change detection accuracy and generalization ability.