Abstract
Texture analysis has been efficiently utilized in the area of terrain classification. In this application, features have been obtained in the 2D image domain. This paper suggests 3D co-occurrence texture features by extending the concept of co-occurrence feature to the 3D world. The suggested 3D features are described as a 3D co-occurrence matrix by using a co-occurrence histogram of digital elevations at two contiguous positions. The practical construction of the co-occurrence matrix limits the number of levels of digital elevation. If the digital elevation is quantized into a few levels over the whole DEM (Digital Elevation Map), distinctive features cannot be obtained. To resolve this quantization problem, we employ the local quantization technique which can preserve the variation of elevations with a small number of quantization levels. Experiments are carried out using an ANN (Artificial Neural Network) classifier, and it is shown that the classification accuracy is significantly improved over the conventional classification methods with 2D features.
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Woo, DM., Park, DC., Han, SS., Nguyen, QD. (2006). Application of 3D Co-occurrence Features to Terrain Classification. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_30
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DOI: https://doi.org/10.1007/11949534_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68297-4
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