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Proceedings Paper

Detection and clustering of features in aerial images by neuron network-based algorithm
Author(s): Vit Vozenilek
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Paper Abstract

The paper presents the algorithm for detection and clustering of feature in aerial photographs based on artificial neural networks. The presented approach is not focused on the detection of specific topographic features, but on the combination of general features analysis and their use for clustering and backward projection of clusters to aerial image. The basis of the algorithm is a calculation of the total error of the network and a change of weights of the network to minimize the error. A classic bipolar sigmoid was used for the activation function of the neurons and the basic method of backpropagation was used for learning. To verify that a set of features is able to represent the image content from the user's perspective, the web application was compiled (ASP.NET on the Microsoft .NET platform). The main achievements include the knowledge that man-made objects in aerial images can be successfully identified by detection of shapes and anomalies. It was also found that the appropriate combination of comprehensive features that describe the colors and selected shapes of individual areas can be useful for image analysis.

Paper Details

Date Published: 9 December 2015
PDF: 5 pages
Proc. SPIE 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015), 98170V (9 December 2015); doi: 10.1117/12.2228918
Show Author Affiliations
Vit Vozenilek, Palacky Univ. Olomouc (Czech Republic)

Published in SPIE Proceedings Vol. 9817:
Seventh International Conference on Graphic and Image Processing (ICGIP 2015)
Yulin Wang; Xudong Jiang, Editor(s)

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