Neural Network Based Terrain Classification Using Wavelet Features

Terrain perception technology using passive sensors plays a key role in enhancing autonomous mobility for military unmanned ground vehicles in off-road environments. In this paper, an effective method for classifying terrain cover based on color and texture features of an image is presented. Discret...

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Bibliographic Details
Published in:Journal of intelligent & robotic systems 2010-09, Vol.59 (3-4), p.269-281
Main Authors: Sung, Gi-Yeul, Kwak, Dong-Min, Lyou, Joon
Format: Article
Language:English
Subjects:
Artificial Intelligence
Classification
Color
Control
Electrical Engineering
Engineering
Mechanical Engineering
Mechatronics
Neural networks
Robotics
Surface layer
Terrain
Texture
Wavelet
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Summary:Terrain perception technology using passive sensors plays a key role in enhancing autonomous mobility for military unmanned ground vehicles in off-road environments. In this paper, an effective method for classifying terrain cover based on color and texture features of an image is presented. Discrete wavelet transform coefficients are used to extract those features. Furthermore, spatial coordinates, where a terrain class is located in the image, are also adopted as additional features. Considering real-time applications, we applied a neural network as classifier and it is trained using real off-road terrain images. Through comparison of the classification performance according to applied feature sets and color space changes, we can find that the feature vectors with spatial coordinates extracted using the Daub2 wavelet in the HSI color space have the best classification performance. Experiments show that using the wavelet features and spatial coordinates features improves the terrain cover classification performance. The proposed algorithm has a promising results and potential applications for autonomous navigation.
ISSN:0921-0296
1573-0409
DOI:10.1007/s10846-010-9402-2