The Identification Of Typical Geohazards In Sichuan Steep Mountainous Based On Remote Sensing Technology And Deep Learning Technology

Landslide,collapse,mudslide is the most common,most typical three-major geological disasters in the high and Tterm Hill,accounting for 98% of the total geological disasters.These three types of geological disasters have a large influence in terrain of terrain,and there is frequently happening in Sichuan Steep Mountainous in our country.The region is the highest level of geological disasters in my country,the most affected losses,is also the key area of my country’s geological disaster prevention and control work,and the typical geological disasters in Sichuan Steep Mountainous are important for geological disaster prevention,reducing people’s lives and property losses.Most of the traditional geological disaster investigation methods are based on point observations,and the time-consuming cost is identified in the wide area range,and the efficiency is low.The rapid development of remote sensing technology provides us with remote sensing imaging for rapid and accurate recognition of geological disasters,not only greatly improved the judgment efficiency of geological disasters,but also through remote sensing images,the coverage is widely implemented.The identification and monitoring of "human are not easy to",the identification and monitoring of high-level minor geological disasters has become an important means of work in my country’s geological disaster monitoring and prevention.In practical applications,first-line technicians face many remote sensing techniques,which are unknown to various technologies.In addition,in a wide range of geological disasters,it is mainly based on manually visualizing the remote sensing image,and the method is highly accurate,but it is extremely expensive.With the promotion and application of modern surveying remote sensing technology,more and more semi-automatic recognition methods have effectively saved human and labor,but due to the low degree of semi-automatic degree,large-scale geological disaster recognition cannot be accurately acquired.For the above problems,the Sichuan Steep Mountainous which is frequently sent by the geological disaster,and conduct research objects to carry out geological disaster recognition research.The main innovations of this paper are as follows:(1)This article innovatively uses a variety of remote sensing technology methods to identify typical geological disasters,discusses the identification characteristics of different geological disasters by different methods,and reveals the applicable scenarios and hidden danger recognition types of different remote sensing technologies.(2)This article innovatively proposes a multi-level debris flow accumulation fan recognition method based on deep convolutional neural networks,constructs a multi-level geological disaster accumulation fan recognition framework,and discusses the impact of scene image size and convolutional layers on recognition accuracy,and an automatic recognition method with higher accuracy and capable of meeting the needs of identifying geological disaster scenes was established.The main research contents and conclusions of this paper are as follows:(1)Based on INSAR(INSAR: Interferometric Synthetic Aperture Radar),drone three-dimensional aerial photography,ground three-dimensional laser and airborne Lidar(Light: Detection and Ranging),etc.,the geological disaster of Sichuan Gao Steps Identification,by comparing the interpretation results,analyze the advantages and disadvantages of different remote sensing techniques,revealing the applicable scenes of different remote sensing techniques,identify hidden tribute types.For unstable slopes that identify difficulty,traditional geological surveys are needed to organically combine modern geological surveys(Li DAR,etc.),through a variety of technical means,on the one hand,by leveraging the advantages of various technical means to achieve the greatest potential for disaster hazards.Comprehensive scanning and identification;on the other hand,the results obtained by various technical means are compared,supplemented,inspected and checked with each other,and finally the hidden dangers of geological disasters are fully and accurately identified.(2)This paper takes the symbolization of Jiuzhaigou as an example,based on Sentinel-2 images,through four methods,supervision classification(maximum likelihood supervision classification,neural network classification method),change detection,and object-oriented classification method to landslide body.The distribution is automatically identified and extracted,and method comparison and precision assessment are performed.Studies have found that this problem is not present in the preparation accuracy,affecting the extraction accuracy,affecting the extraction accuracy,and the object-oriented classification method does not exist.When the ground object spectrum reflection of the remote sensing image is different,several methods are not large to the recognition accuracy of the newly generated ground object after the shock.The overall recognition accuracy is greater than 94.00%,and the Kappa coefficient is greater than 0.800.On this basis,the principle of shock landslide intelligence recognition is achieved based on the pixel matrix difference calculation and the binary image division.(3)Aiming at the automatic identification method of geological disasters,taking the identification of debris flow accumulation fan as an example,deep learning technology is introduced to realize the automatic identification of hidden dangers of geological disasters.Using the multi-level debris flow accumulation fan recognition method proposed in this paper,the deep convolutional neural network can automatically identify debris flow accumulation fans and improve the accuracy of geological disaster identification scenes.The research results can provide data support and reliable technical support for regional geological disaster monitoring and early warning,engineering governance,disaster prevention and mitigation,and urban and rural planning,which can effectively guide economic construction and economic development.The network technology provides a reference for the research of high-precision identification of potential hidden dangers.