期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2015
卷号：XL-7/W3
页码：1353-1357
DOI：10.5194/isprsarchives-XL-7-W3-1353-2015
出版社：Copernicus Publications
摘要：Urban density is an important factor for several fields, e.g. urban design, planning and land management. Modern remote sensors deliver ample information for the estimation of specific urban land classification classes (2D indicators), and the height of urban land classification objects (3D indicators) within an Area of Interest (AOI). In this research, two of these indicators, Building Coverage Ratio (BCR) and Floor Area Ratio (FAR) are numerically and automatically derived from high-resolution airborne RGB orthophotos and LiDAR data. In the pre-processing step the low resolution elevation data are fused with the high resolution optical data through a mean-shift based discontinuity preserving smoothing algorithm. The outcome is an improved normalized digital surface model (nDSM) is an upsampled elevation data with considerable improvement regarding region filling and “straightness” of elevation discontinuities. In a following step, a Multilayer Feedforward Neural Network (MFNN) is used to classify all pixels of the AOI to building or non-building categories. For the total surface of the block and the buildings we consider the number of their pixels and the surface of the unit pixel. Comparisons of the automatically derived BCR and FAR indicators with manually derived ones shows the applicability and effectiveness of the methodology proposed.
关键词：urban density; LiDAR; Neural Networks; classification; land management
