Comparing color and textural information in very high resolution satellite image classification
With the advent of very high resolution satellite images, such as IKONOS, the question of how we can incorporate textural information in classifying and segmenting different regions has become of great interest, in this paper we compare the power of classifying regions based on using color information alone to using texture and color texture information. We use a 2D and 3D extension of the co-occurrence matrix and the features derived from them. In the latter case the effect of color space reduction is also evaluated. We found that although color features perform best in the easy classification tasks, very high classification rates are obtained using color texture features and the fragmentation degree in the classified areas is smaller.