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. With the addition of 3D co-occurrence features, we encounter the high dimensionality problem in the classification process. Since these ANN (Artificial Neural Networks) clustering algorithms are known as robust in this situation, FCM (Fuzzy C-mean) and GBFCM (Gradient Based Fuzzy C-mean) clustering algorithms are employed to implement the terrain classifier. Experimental results show that the classification accuracy with the addition of 3D co-occurrence features is significantly improved over the conventional classification method only with 2D features. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Woo, D. M., Park, D. C., Song, Y. S., Nguyen, Q. D., & Tran, Q. D. N. (2007). Terrain classification based on 3D Co-occurrence features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4681 LNCS, pp. 1266–1274). Springer Verlag. https://doi.org/10.1007/978-3-540-74171-8_129
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