Research On Monitoring Technology Of Railway Construction Temporary Building With High Resolution Remote Sensing Imagery

Due to the long construction period of railway construction,large area occupation,and high resource consumption,temporary buildings need to be monitored to avoid greater impact on the surrounding environment and ecology.At present,most of the monitoring of temporary construction of railway construction adopts the method of manual investigation.This method requires a lot of manpower and material resources,and is affected by environmental and climatic conditions.It cannot comprehensively,on time,and dynamically monitor temporary buildings.Using high-resolution remote sensing images and remote sensing technology with the advantages of high resolution,wide coverage,strong real-time performance,and low cost to monitor temporary buildings can effectively make up for the shortcomings of traditional monitoring methods.This dissertation uses remote sensing technology to monitor the temporary construction of railway construction.The main research contents are as follows:(1)High-resolution remote sensing image classification and identification of temporary construction of railway construction.The primary condition for the monitoring of temporary buildings is to identify the temporary buildi ngs in the study area.This dissertation proposes an object-oriented multi-feature ABC-SVM remote sensing image classification method.Using multi-temporal remote sensing images and post-classification comparison method to dynamically and accurately monitor temporary buildings.The method first uses remote sensing image segmentation with improved Canny edge detection to segment the image.Then use artificial bee colony algorithm(Artificial Bee Colony,ABC)optimized support vector machine(SVM)supervised classification method to classify multiple time-phase remote sensing images during railway construction.(2)High-resolution remote sensing image monitoring of changes and demolition of temporary railway construction.This dissertation proposes a multi-level CVA(Change Vector Analysis,CVA)remote sensing image change detection method based on low-rank decomposition and SLIC(Simple Linear Iterative Clustering,SLIC)multi-scale segmentation for the extraction of change information of temporary building land occupation along the railway line.Multi-temporal remote sensing images are used to further monitor the changes and demolishment of temporary buildings during various periods of railway construction.The proposed method mainly solves three problems.First,for the problem that the change detection accuracy is affected b y the segmentation results,this dissertation proposes a multi-scale remote sensing image segmentation method.Second,a two-stage sparse and low-rank matrix decomposition method is used to remove redundant information in the image,reduce the amount of calculation,and reduce the impact of noise on the results.Third,the adaptive fusion multi-level CVA change detection method is used to process the changes of multi-temporal images to improve the accuracy of change detection.The experimental results from visual interpretation and quantitative indicators show that the algorithm has higher detection accuracy and can help the monitoring personnel to monitor the changes and demolition of temporary buildings.(3)Design and implementation of railway remote sensing environmental monitoring system.This paper uses plug-in technology to carry out secondary development on the basis of MapWindowGIS to complete the design and implementation of the temporary building remote sensing monitoring subsystem in the railway environment monitoring system.