TERRAIN CLASSIFICATION USING 3D CO-OCCURRENCE FEATURES AND NEURAL NETWORKS

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. In this paper we suggest 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. SOM (Self-Organizing Maps), FCM (Fuzzy C-mean) and GBFCM (Gradient Based Fuzzy C-mean) clustering algorithms are employed to implement the terrain classifier, since these ANN clustering algorithms are known as robust against the high dimensionality problem in the classification process. 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.