Study Of Land Cover And Building Density Based On Airborne Sensor Data In Economic And Technological Development Zone

The waste and low efficiency of industrial land-use has become a critical factor of preventing social economic development and contradiction between land resource supply and demand. The extensive land-use type should be changed. The authenticity of data traditionally provided by land consuming companies becomes increasingly questioned under the background of enhancing monitoring and improving efficiency of land use. As the development of airborne remote sensing technology, the latest LiDAR sensor platform has been recognized as the most promising tools for building extraction in urban area. The property of fast, objective, real-time, low cost and labor saving make it extremely suitable for building density estimation in Economical and Technological Development Zone.So far, much effort has been made to the study of building extraction based on LiDAR data, but few attention has been paid to building type classification as well as the estimation of building density in Economic and technological Development Zone. This study focus on the methods of building type classification and building density estimation through two approaches:using LiDAR data alone and the combined use of LiDAR data and RGB imagery. The core points and conclusions are listed as follows:1LiDAR points are3D sampling of the earth surface, spatial autocorrelation features derived from this data can characterize the properties of autocorrelation and clustering of objects with high or low elevation. Various sources of features including geometric, autocorrelation and height heterogeneity were integrated on the recognition and classification of buildings through an object oriented approach and post classification technique based solely on LiDAR data. Variable selection and importance measure by random forest classifier were also participated in this process. Accuracy assessment indicated the successful extraction of industrial/residential buildings. Overall classification accuracy of the two blocks were both exceed90%with Kappa value more than0.88. The proposed method was demonstrated to be stable and reliable. 2In fact, residential and industrial buildings are the predominantly distributed anthropic structure types in Chinese urban region, and present obvious differences in spatial size and pattern which can be identified through building based lacunarity technique. First, the area of residential buildings is generally smaller than that of industrial buildings in Chinese ETDZs. In parcels of land of equal size, the number of buildings and the total area of gap are typically larger in residential areas. Second, residential buildings are often regularly distributed, whereas industrial buildings tend to be disorganized.Vegetation mask was generated firstly through classification by using a SVM classifier and an integration of Gabor, GLCM features and RGB imagery. Vegetation objects were then masked out with this vegetation mask. Buildings were thus left. A binary map containing building/non-building was created after some simple editing of the building layer. Lacunarity features that indicate percent and geometry of building gaps were extracted based on this binary map and then applied in building type classification. Vegetation mask was assessed to be high quality indicated by an overall accuracy of86%and a Kappa statistic of more than0.7. The integration of multispectral image and texture was illustrated to be successful for vegetation extraction. Overall classification accuracy of lacunarity group were both exceed85%. Kappa analysis result demonstrated the superiority of lacunarity to nDSM on building type classification in95%confidence level.3The floor height was one of the key factors to the accuracy of Building Density estimation. The number of floors were commonly determined by setting a unique threshold as floor height. This parameter was also defined with reference to the location of buildings in some few studies. Nevertheless, the former shall inevitably increase the residuals of building density while the latter suffers from human interaction. Because floor height is mostly correlated to building type, it was defined according to the building type in this study to estimate building density more accurately and automatically.4To those complicate structure buildings with multiple floor heights, we proposed a method that involves small scale segmentation and building density estimation based on those small segments. Overestimation error was thus significantly reduced when the whole building polygon was participated as basic unit for building density estimation.5The accuracy assessment on Building Density based on RMSE?Correlation Analysis and Residual Analysis illustrated a satisfactory result with the overall error below0.2, regression coefficient greater than0.8and residual error less than15%.