Research On Land Rise Information Extraction And Spatial Distribution Characteristics Of Honggu District Based On Improved DeepLab V3+

Information of land use remote sensing image is to obtain the large space scale,the main data.source of remote sensing image processing of land use in land resources management segmentation result,precision agriculture,urban and rural development planning:geographic space object detection envionmental protection and land ecological problem analysis plays an important role in many applications.Land use mode has a certain influence on the formation and development of landform and landform also affects the spatial pattern and evolution of land use types to acertain extent.The research of land use is beneficial to the rational allocation of land resources.However.with the continuous development of sat ellite remote sensing technology and remote sensing platform.the spatial and temporal resolution of remote sensing image is continuously improved,and the available high-resolution image is increasing day by day.The remote sensing image data obtained by high-resolution remote sensing satellite has rich texture information,spatial information and more obvious geometric features of the ground.With the continuous improvement of image resolution and the gradual enrichment of acquisition methods,how to extract land use inform at ion from remote sensing images quickly and effectively has become a key and difficult problem in current research.In recent years,the rapid rise of deep learning image segmentation technology has realized the automatic and effective extraction of shallow and deep semantic information in images,which provides technical support for accurate and efficient classification of high-resolution remote sensing images.In this paper,the DeepLab V3+model in the deep learning semantic segmentation,model is studied taking the land use types of Honggu district as the research object.Firstly.ArcGIS is used to vector label the land types in the images.Then:ArcGIS Pro deep learning tool was used to cut out the samnple data set suitable for deep learning training Then,DeepLab V3+ semantic segmentation model and mainstream deep Learning model were used to segment the land use types in the study area and the DeepLab V3+ model was optimized.Finally,the influence of topographic factors on the spatial distribution of land use is analyzed This article main research content is as follows:(1)The image is manually vectorized to obtain the label data meeting the deep learning training.and the ArcGIS Pro software is used to cut the image data and the acquired label data with 128×128 sliding window to obtain a tile data set of 256x256 size.The obtained data sets are processed out of order and enhanced and the data sets are divided according to a certain proportion to complete the production of the data set,which is used for deep learning model training.The ArcGIS Pro sofware is used for data processing and the dataform conforms to most remote sensing image processing tools which is convenient for further research on land use data in the later stage.(2)DeepL ab V3+semantic segmentation model was used to train the land use data,in Honggu district,and compared with the mainstream PSPNet,ICNet and U-Net deep learning models The results show that the DeepLab V3+model with atrous convolution structure is better for land use segmentation by considering the two factors of velocity and efficiency.(3)The original DeepLab V3+model was improved:and the Xception backbone network for feature extraction was replaced with the ResNet network.Compared with the original DeepLab V3+model and the mainstream PSPNet ICNet and U-Net models,the results show that the.improved DeepLab V3+model can improve the performance of network segmentation to a certain extent.Adjust the learning rate decline method(including Piecewise,Ploy and Cosine),the normalization method(BN and GN)and the optimization algorithm(Adam and SGD)to get the optimal combination suitable for land use informtion extraction.The results show that the combination of BN,Adam and Plov has the best effect.(4)The DEM data.of Honggu district were used to extract six topographic factors,namely elevation,slope,aspect,relief amplitude,surface cutting depth and surface roughness.The degree of fragmentation and stability of land use spatial distribution in this region were analyzed by calculating the morphological dimension of land use type and the fractal dimension and stability index.By superimposed analysis of the extracted six topographic factors and land use data,the distribution index and the distribution proportion of each land use type under different topographic conditions were calculated to analyze the spatial distribution characteristics of land use in the Honggu distrct.The results show that the three kinds of microscopic topographic factors have different effects on the spatial distribution of land use types.The influences of the three macroscopic topographic factors on the spatial distribution of land use types were consistent.To sum up,deep learning data,set was constructed by manual annotation in this paper and used for deep learning model training.Through comparison,it was found that the improved DeepLab V3+segmentation result was better,which provided technical support for the research on ground object segmentation of remote sensing images and foundation for the research on spatial distribution charact eristics of land use in the later stage.