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Object-based detection of vehicles using combined optical and elevation data

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


ISPRS Journal of Photogrammetry and Remote Sensing 136 (2018), pp.85-105
ISSN: 0924-2716
Journal Article
Fraunhofer IOSB ()
Vehicle detection; object-based classification; data fusion; elevation data; random forest; high-resolution; feature extraction; cluster analysis

The detection of vehicles is an important and challenging topic that is relevant for many applications. In this work, we present a workflow that utilizes optical and elevation data to detect vehicles in remotely sensed urban data. This workflow consists of three consecutive stages: candidate identification, classification, and single vehicle extraction. Unlike in most previous approaches, fusion of both data sources is strongly pursued at all stages. While the first stage utilizes the fact that most man-made objects are rectangular in shape, the second and third stages employ machine learning techniques combined with specific features. The stages are designed to handle multiple sensor input, which results in a significant improvement. A detailed evaluation shows the benefits of our workflow, which includes hand-tailored features; even in comparison with classification approaches based on Convolutional Neural Networks, which are state of the art in computer vision, we could obtain a comparable or superior performance (F1 score of 0.96-0.94).