Abstract
Abstract. In the urban object detection challenge organized by the ISPRS WG III/4 high geometric and radiometric resolution aerial images about Vaihingen/Stuttgart, Germany are distributed. The acquired data set contains optical false color, near infrared images and airborne laserscanning data. The presented research focused exclusively on the optical image, so the elevation information was ignored. The road detection procedure has been built up of two main phases: a segmentation done by neural networks and a compilation made by genetic algorithms. The applied neural networks were support vector machines with radial basis kernel function and self-organizing maps with hexagonal network topology and Euclidean distance function for neighborhood management. The neural techniques have been compared by hyperbox classifier, known from the statistical image classification practice. The compilation of the segmentation is realized by a novel application of the common genetic algorithm and by differential evolution technique. The genes were implemented to detect the road elements by evaluating a special binary fitness function. The results have proven that the evolutional technique can automatically find major road segments.
Highlights
The working group III/4 of ISPRS Commission III has conducted a challenge of detecting man-made objects from digital aerial imagery
The most used neural networks in image processing have a strong focus on classification, which is appropriate application for back-propagation (BP) network, radial basis function (RBF) networks, learning vector quantization (LVQ) networks and support vector machines (SVM)
The unsupervised category is important: e.g. self-organizing maps (SOM), competitive learning networks. [Beale et al, 2012] The current paper presents applications of SVM and SOM technologies
Summary
The working group III/4 of ISPRS Commission III has conducted a challenge of detecting man-made objects from digital aerial imagery. These objects are urban objects (roads, trees, cars etc.) and 3D buildings. This challenge has been supported by adequate data sets: there was an aerial photography campaign near Stuttgart, Germany, when color images and LIDAR-point clouds were acquired. The raw data were processed: the aerotriangulation was calculated, and digital elevation model (DEM) was derived. All of these products can be downloaded and used for developing object detection methodologies. The general theory of the two artificial intelligence tools is presented, followed by the details of the applied methodology in image analysis
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