Improved NDBI differencing algorithm for built-up regions change detection from remote-sensing data: an automated approach

Abstract

Remote-sensing-based multi-temporal satellite images allow the mapping of changes in built-up areas over time to illustrate the urban development. An improved normalized difference built-up index (NDBI) has recently been promoted as a more effective algorithm to identify built-up regions, compared with the conventional NDBI approach. The conventional NDBI algorithm assumes that difference between the values of the binary NDBI and binary normalized difference vegetation index (NDVI) would indicate built-up areas. The modified NDBI approach improves this assumption by assigning higher positive difference values of continuous NDBI and NDVI to built-up region using an optimal threshold value. This article extends the concept of improved NDBI approach to automate the extraction of built-up changes using multi-temporal satellite images. An automated kernel-based thresholding algorithm is used to sort the difference values of multi-temporal built-up image, obtained from modified improved NDBI differencing algorithm, into built-up and no-built-up change regions for enhancing the efficiency of built-up change detection process. The improved NDBI differencing algorithm better detects built-up change regions than original NDBI differencing algorithm. As a case study, the proposed algorithm has been implemented on Landsat-5 Thematic Mapper (TM) images of a typical Indian city and surrounding areas for built-up change detection.