A Comparative Study of Built-up Index Approaches for Automated Extraction of Built-up Regions From Remote Sensing Data

Accurate built-up information is imperative for loss estimation and disaster management after the occurrence of catastrophic events such as earthquake, tornado, tsunami and flood. These catastrophic events leave behind a trail of mass destruction with property and human losses amounting to millions. Once a natural disaster hits a region, built-up information is required within a short span of time for disaster management. Nowadays, earth observation satellite imagery serves as a promising source to extract the land use / land cover classes. However, the automatic extraction of urban built-up from remote sensing data is a known challenge in the remote sensing community. The normalized difference built-up index (NDBI) algorithm has been recognized as an effective algorithm for automatic built-up identification from medium spatial and spectral resolution satellite images. Few researchers have modified this algorithm and proposed new quantitative expressions for the built-up index. In this paper, three built-up index based, unsupervised built-up extraction algorithms have been reviewed and compared. An automated kernel-based probabilistic thresholding algorithm is used to assort the built-up index values, obtained from modified built-up index algorithms, into built-up and non built-up regions for enhancing the efficiency of the built-up detection process. Qualitative assessment of these algorithms involves computation of several parameters including recently developed parameters like allocation disagreement and quantity disagreement, and classical parameters such as error of omission, error of commission and overall accuracy. This paper presents a case study where the algorithms have been implemented on Landsat-5 Thematic Mapper (TM) image of the city of Delhi and its surrounding areas for detection of built-up regions automatically.