The Pruning of Combined Neighborhood Differences Texture Descriptor for Multispectral Image Segmentation - Saipullah, Khairul Muzzammil (2011)
This paper proposes a novel feature extraction method for unsupervised multispectral image segmentation by pruning the two dimensional texture feature named combine neighborhood differences. In contrast with the original CND, which is applied with traditional image, the pruned CND is computed on a single pixel with various bands. The proposed algorithm utilizes the sign of differences between bands of the pixel. The difference values are thresholded to form a binary codeword. A binomial factor is assigned to the codeword to form another unique value. These values are then grouped to construct the multiband CND feature image where is used in the unsupervised segmentation. Experimental results, with respect to segmentation and classification accuracy using two LANDSAT multispectral images test suite have been performed. The result shows that the pruned CND feature outperforms spectral feature, with average classification accuracies of 87.55% whereas that of spectral feature is 55.81%.