Road Extraction From Cartosat-2f Multispectral Data With Object-Oriented Analysis

For detection of a road network, high-resolution satellite data have been used following the object-oriented classification approach. We used object-based feature extraction algorithms for detection of road networks from a high resolution Cartosat-2F multispectral data in an Indian city with varying terrain conditions ranging from a compact built-up area to a predominantly vegetated area. The approach involves multi-resolution segmentation (MRS) and spectral difference segmentation (SDS) followed by road extraction using fuzzy rule-based algorithm based on various object features, viz. gray-level co-occurrence matrix homogeneity, density, rectangular fit, etc. With overall accuracies ranging from 77.46% to 92% SDS approach performed better than MRS which could afford 60.46% to 75.0% only. However, both of these approaches score over the classical Gaussian maximum likelihood classifier which could register only 50.0% to 68.0% overall accuracy. Furthermore, the maximum overall accuracy was obtained in compact built-up site (85% to 92%) followed by sparsely built-up site (75.0% to 88%) and predominantly vegetated site (60.46% to 77.14%).