The multifaceted data from Indian remote sensing satellite (IRS)-1C with Panchromatic (PAN, 5.8 m, 0.50-0.75 lm), Linear Imaging Self-Scanning Sensor-III (LISS-III, 23.5 m, four multi-spectral bands) and Wide Field Sensor (WiFS, 188 m, red and near-infrared bands) sensors onboard, along with stereo imaging and 5-day revisit capability have been effectively utilised for mapping, monitoring, planning and development of urban and regional areas. It made possible to explore urban and regional areas either independently or in combination with other remote sensing data for base map preparation, land use survey and planning, growth modelling, environment and hazards analysis, utilities and infrastructure planning, etc. This review article articulates the scale and mapping potential of IRS-1C data for urban and regional areas, data fusion methods and information retrieval based on visual or digital image processing techniques, including advanced classifiers. It recapitulates the application potential demonstrated in last 25 years in urban and regional studies, and for infrastructure planning.
<p><strong>Abstract.</strong> Monitoring the urban development/change is of critical importance in planning the future infrastructure of a city. The use of satellite images in urban related studies has yielded in exemplary results. The city of Bengaluru, with high variation in urban landscape is most suited for this study. In this paper, the potential of the SAR imagery in understanding and characterizing the urban features is studied. The SAR images have unique characteristics such as double bounce and corner reflectors which are prominent in an urban landscape. The diverse urban features are characterised by comparing the graphs derived from the image statistics of temporal Sentinel-1 dual polarized data. For the generation of the urban footprint a rule based approach and an object oriented approach has been implemented in this study. The stack of coherence image and synthetic bands derived from image statistics of the VV polarization is used as the input image for the same. The final urban footprint is derived by the comparison of the output from both the methods. The results are authenticated with the urban footprint obtained by optical imagery of the same area for better understanding and improvement of the algorithm. The observations are made regarding the contribution of SAR in the study of urban features and the feasibility of implementation in the mainstream analysis.</p>
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