In this study, a mechanism for quantifying the uncertainty associated with image classification is suggested. In
image classification process, an uncertainty measure can be obtained for each pixel thereby indicating that spatial
distribution of quality can also be modelled using uncertainty measures. It thus provides an extended information about
the quality of spatial data unlike various accuracy measures which indicate quality on map basis or individual class basis
(Steele et al., 1998).
The differential SAR interferometry (DInSAR) has been increasing used to monitor ground surface
displacements, which may be caused by various natural disasters such as earthquakes, landslides, mining activities,
avalanches etc. Conventionally, these displacements were being estimated through field measurements, which are time
consuming, hazardous and with data collected over few point locations. Since all the development and rehabilitation
works after a natural disaster strikes is carried out on regional basis, any information at spatial level is advantageous in
planning, management and monitoring activities. In recent years, the application of Differential SAR interferometry is
gaining momentum to estimate the surface displacements at millimeter level accuracy. The displacement maps produced
via this technique provide information at spatial level in the region thereby assisting in judicious developmental and
planning works in an efficient and cost-effective manner. The aim of this paper is provide an overview of the use of
Differential SAR Interferometry (DinSAR) technology for the study of surface displacements. As a case study, land
subsidence occurred due to coal mining in Jharia coal fields, Jharkhand, have been estimated through this technique. All
the procedural steps in implementing the approach based on DinSAR have been explained in a simplified manner.
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