Extracting Built\|up Areas from TerraSAR-X Data Using Object-oriented Classification Method

Urban sprawl stands for one of the most dynamic process in the context of global land use changes.Currently developing countries are going through the tide of urban expansion，represented by China and India.The constantly increasing loss of land resources due to growing settlements comes along with various ecological and socioeconomic challenges such as air pollutant，water contamination，urban heat island effect and urban waterlog disaster.In order to prevent these negative consequences，effective methods and strategies for a sustainable development of urban planning is the availability of accurate and up\|to\|date geo\|data on the location，shape，and dynamics of built\|up areas.Based on single\|polarized TerraSAR\|X，the approach generates homogeneous segments on an arbitrary number of scale levels by applying a region\|growing algorithm，which takes the intensity of backscatter and shape\|related properties into account.The object\|oriented procedure consists of three main steps:firstly，the analysis of the local speckle behavior in the SAR intensity data，leading to the generation of a texture image；secondly，a segmentation based on the intensity image；thirdly，the classification of each segment using the derived texture file and intensity information in order to identify and extract build\|up areas.In our research，the distribution of BAs in Dongying City is derived from single\|polarized TSX SM image (acquired on 17th June 2013)with average ground resolution of 3m using our proposed approach.By cross\|validating the random selected validation points with geo\|referenced field sites，Google Earth high\|resolution imagery，confusion matrices with statistical indicators are calculated and used for assessing the classification results.The result of kappa coefficient is 0.85，OA coefficient is 92.89%.We have shown that connect texture information with the analysis of the local speckle divergence，combining texture and intensity of construction extraction is feasible，efficient and rapid.