Performance testing of selected spectral indices in automated extraction of impervious built-up surface features using Resourcesat LISS-III image

The increasing urban density and spatial expansion of urbanized areas result in changes from the natural landscape to impervious surface features. Remote sensing–based spectral indices provide an efficient method in the automated identification of land use and cover classes. However, a common challenge is the accurate extraction of built-up features from satellite images. The original Normalized Difference Built-up Index (NDBI) has been modified by several researchers in the anticipation of improvement of the built-up area classification. The indices adopted in the study are Index-based Built-up Index (IBI), Built Up Index (BUI), NDBI, Modified Built-up Index (MBI), and the newly developed Impervious Built-up Index (IBUI). These indices work on automated kernel-based probabilistic thresholding algorithm to group the index values into built-up and non-built-up areas. This study investigates the performance of the above mentioned five spectral indices on Resourcesat LISS III imageries over the Kolkata-Howrah urban agglomeration, India. The indices were compared and found based on the overall accuracy that the novel IBUI provided the maximum accuracy (94%) in extracting impervious built-up areas. Based on the overall accuracy and kappa statistic the IBUI was chosen for estimating spatio-temporal urban growth of the study area from 2003 to 2018 based on the same LISS-III sensor data. It was calculated that the urban built-up area has increased from 144 to 177 km2.