Incorporating DeepLabv3+and object-based image analysis for semantic segmentation of very high resolution remote sensing images  

作　　者：Shouji Du Shihong Du Bo Liu Xiuyuan Zhang 

机构地区：[1]Institute of Remote Sensing and GIS,Peking University,Beijing,People’s Republic of China

出　　处：《International Journal of Digital Earth》2021年第3期357-378,共22页国际数字地球学报（英文）

基　　金：was funded by the Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Ministry of Natural Resources[grant number 2020-2-1];the National Natural Science Foundation of China[grant number 41871372].

摘　　要：Semantic segmentation of remote sensing images is an important but unsolved problem in the remote sensing society.Advanced image semantic segmentation models,such as DeepLabv3+,have achieved astonishing performance for semantically labeling very high resolution(VHR)remote sensing images.However,it is difficult for these models to capture the precise outlines of ground objects and explore the context information that revealing relationships among image objects for optimizing segmentation results.Consequently,this study proposes a semantic segmentation method for VHR images by incorporating deep learning semantic segmentation model(DeepLabv3+)and objectbased image analysis(OBIA),wherein DSM is employed to provide geometric information to enhance the interpretation of VHR images.The proposed method first obtains two initial probabilistic labeling predictions using a DeepLabv3+network on spectral image and a random forest(RF)classifier on hand-crafted features,respectively.These two predictions are then integrated by Dempster-Shafer(D-S)evidence theory to be fed into an object-constrained higher-order conditional random field(CRF)framework to estimate the final semantic labeling results with the consideration of the spatial contextual information.The proposed method is applied to the ISPRS 2D semantic labeling benchmark,and competitive overall accuracies of 90.6%and 85.0%are achieved for Vaihingen and Potsdam datasets,respectively.

关 键 词：Semantic segmentation DeepLabv3+ object-based image analysis DempsterShafer evidence theory conditional random field VHR images 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]