Abstract
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. In this context, FCM (Fuzzy C-mean) clustering algorithm is employed to implement the terrain classifier, since this ANN (Artificial Neural Networks) clustering algorithms is known as robust in this particular situation. 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.
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