Urban building information can be effectively extracted by applying object-based image segmentation and multi-stage thresholding on High Resolution (HR) remote sensing satellite imageries. This study provides the results obtained using this method on the images of Indian remote sensing satellite, CARTOSAT-2S launched by the Indian Space Research Organization (ISRO). In this study, a method is developed to extract urban building footprints from the HR remote sensing satellite images. The first step of the process consists of generating highly dense per pixel Digital Surface Model (DSM) by using semi global matching algorithm on HR satellite stereo images and applying robust ground filtering to generate Digital Terrain Model (DTM). In the second step, multi-stage object-based approach is adopted to extract building bases using the PAN sharpened image, normalized Digital Surface Model (nDSM) derived from DSM and DTM, and Normalised Difference Vegetation Index (NDVI). The results are compared with the manual method of drawing building footprints by cartographers. An average precision of 0.930, recall of 0.917, and f-score of 0.922 are obtained. The results are found to be in a match with the method using the high resolution Airborne LiDAR DSM by providing a solution for large areas, low cost and low time.

The purpose of this study is to investigate the best suitable pan sharpening method for CARTOSAT-2E satellite launched by ISRO (Indian Space Research Organisation). This satellite provides high resolution images that are being used for many urban applications such as mapping, feature extraction, facility management etc. The synthesized image using pan sharpening method enables users to take the combined advantage of the best available spatial and spectral resolutions. In this paper, various pan sharpening methods based on component substitution (CS) and Multi Resolution Analysis (MRA) are applied on the CARTOSAT-2E images and the resultant images are tested for their qualitative and quantitative performance. Qualitative analysis is carried out based on image blur and spectral distortion and quantitative evaluation is performed using image metrics by comparing the synthesized image with the original image. The results show that the High-Pass Filter (HPF) method offers the good spectral fidelity. However, due to its inherent stair-casing effect in the resultant image; modified-IHS followed by PRACS method is found to be preferable for automatic urban feature extraction from CARTOSAT-2E images.