Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Object-based detection of vehicles in airborne data

: Schilling, Hendrik; Bulatov, Dimitri; Middelmann, Wolfgang

Postprint urn:nbn:de:0011-n-3789173 (591 KByte PDF)
MD5 Fingerprint: 60fd0ec1ada201a14973226e958326af
Copyright Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 16.2.2016

Bruzzone, L. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.; European Association of Remote Sensing Companies -EARSC-:
Image and signal processing for remote sensing XXI : 21 - 23 September 2015, Toulouse, France
Bellingham, WA: SPIE, 2015 (Proceedings of SPIE 9643)
ISBN: 978-1-62841-853-8
Paper 964316, 10 S.
Conference "Image and Signal Processing for Remote Sensing" <21, 2015, Toulouse>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
3D reconstruction; classification; detection; vehicles

Robust detection of vehicles in airborne data is a challenging task since a high variation in the object signatures - depending on data resolution - and often a small contrast between objects and background lead to high false classification rates and missed detections. Despite these facts, many applications require reliable results which can be obtained in a short time. In this paper, an object-based approach for vehicle detection in airborne laser scans (ALS) and photogrammetrically reconstructed 2.5D data is described. The focus of this paper lies on a robust object segmentation algorithm as well as the identification of features for a reliable separation between vehicles and background (all none-vehicle objects) on different scenes. The described method is based on three consecutive steps, namely, object segmentation, feature extraction and supervised classification. In the first step, the 2.5D data is segmented and possible targets are identified. The segmentation progress is based on the morphological top-hat filtering, which leaves areas that are smaller than a given filter size and higher (brighter) than their surroundings. The approach is chosen due to the low computational effort of this filter, which allows a fast computation even for large areas. The next step is feature extraction. Based on the initial segmentation, features for every identified object are extracted. In addition to frequently used features like height above ground, object area, or point distribution, more complex features like object planarity, entropy in the intensity image, and lineness measures are used. The last step contains classification of each object. For this purpose, a random forest classifier (RF) using the normalized features extracted in the previous step is chosen. RFs are suitable for high dimensional and nonlinear problems. In contrast to other approaches (e.g. maximum likelihood classifier), RFs achieves good results even with relatively small training samples.