Abstract
Abstract. The paper describes two different methods for extraction of two types of urban objects from lidar digital surface model (DSM) and digital aerial images. Within the preprocessing digital terrain model (DTM) and orthoimages for three test areas were generated from aerial images using automatic photogrammetric methods. Automatic building extraction was done using DSM and multispectral orthoimages. First, initial building mask was created from the normalized digital surface model (nDSM), then vegetation was eliminated from the building mask using multispectral orthoimages. The final building mask was produced employing several morphological operations and buildings were vectorised using Hough transform. Automatic extraction of other green urban features (trees and natural ground) started from orthoimages using iterative object-based classification. This method required careful selection of segmentation parameters; in addition to basic spectral bands also information from nDSM was included. After the segmentation of images the segments were classified based on their attributes (spatial, spectral, geometrical, texture) using rule set classificator. First iteration focused on visible (i.e. unshaded) urban features, and second iteration on objects in deep shade. Results from both iterations were merged into appropriate classes. Evaluation of the final results (completeness, correctness and quality) was carried out on a per-area level and on a per-object level by ISPRS Commission III, WG III/4.
Highlights
1.1 Motivation and aimsUrban systems are very complex and are composed of a large number of spatially heterogeneous components
In attempt to provide results closely matching to the reference data two approaches were implemented: a method for automatic building extraction and an object based-classification based on rule set classifier for automatic vegetation extraction
We were not able to detect some smaller buildings from the derived normalized digital surface model (nDSM)
Summary
Urban systems are very complex and are composed of a large number of spatially heterogeneous components By their very nature, such systems require advanced methods and algorithms in order to obtain results closer to automatic extraction. In March 2011 ISPRS Commission III, WG III/4 provided test data to the participants of the ISPRS Test Project on Urban Classification and 3D Building Reconstruction in order to evaluate techniques for the extraction of various urban object classes. The aim of this project was to analyse state-of-the-art data sets which were used to obtain urban objects with selected methods and algorithms. In attempt to provide results closely matching to the reference data two approaches were implemented: a method for automatic building extraction and an object based-classification based on rule set classifier for automatic vegetation extraction
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