Object Detection Using Neural Self-organization

The paper presents a novel artificial neural network type, which is based on the learning rule of the Kohonen-type SOM model. The developed Self-Organizing Neuron Graph (SONG) has a flexible graph structure compared to the fixed SOM neuron grid and an appropriate training algorithm. The number and structure of the neurons express the preliminary human knowledge about the object to be detected, which can be checked during the computations. The inputs of the neuron graph are the coordinates of the image pixels derived by different image processing operators from segmentation to classification. The newly developed tool has been involved in several types of image analyzing tasks: from detecting building structure in high-resolution satellite image via template matching to the extraction of road network segments in aerial imagery. The presented results have proved that the developed neural network algorithm is highly capable for analyzing photogrammetric and remotely sensed data.