Satellite image classification based on Gabor texture features and SVM

The texture is a very important factor in region-based segmentation of images. Texture Applications include industrial inspection, estimation of object range and orientation, shape analysis, satellite imaging, and medical diagnosis. In this paper we present a methodology based on computing a set of textural measures with Gabor filter. The time-frequency transformed based method of texture discrimination, which is in turn based on Gabor filters is done. In Gabor transform, a signal can be represented in terms of sinusoids that are modulated by translated Gaussian windows. In this paper, the Gabor texture features combined with original bands of image, PCA, and NDVI were adopted as the characteristic vector of training samples for SVM, and Decision Tree classification. Finally, traditional classification schemes of Maximum Likelihood were comparatively studied. For most of the cases, the SVM method gave the highest correct classification rate within these three methodologies. Decision tree and SVM have their superiority respectively.