The role of texture information and data fusion in topographic objects extraction from satellite data

Abstract

The growing availability of the satellite data has augmented the need of information extraction that can be utilized in various application including topographic map updation, city planning, pattern recognition and machine vision etc. The accurate information extraction from satellite images involves the integration of additional measures such as texture, shape etc. In this paper, investigation on extraction of topographic objects from satellite images by incorporating the texture information and data fusion has been made. The applicability of various texture measures based on the gray level co-occurrence matrix along with the effect of varying pixel window is also discussed. The classification results indicate that homogeneity texture image generated using 3*3 window size is best suitable for topographic objects extraction. The best classification results with overall accuracy 85.0% and kappa coefficient 0.80 are obtained when classification is performed on fused image (Multispectral + PAN + Texture).