Abstract: The fast expansion of urban built-up land and accompanied sharp decrease in farm land have made timely monitoring of landuse changes become more important than ever before.The ability to monitor the built-up land dynamics in a cost-effective manner is highly desirable for local communities and decision makers alike.Fortunately,satellite remote sensing technique offers considerable promise to meet this requirement.Although the use of remote sensing technique in the monitoring of land use changes has become more and more popular and satellite imagery has been frequently used to discriminate built-up lands from non-built-up lands for the last few decades,the extraction of built-up land information from remote sensing imagery is still not an easy task due largely to the heterogeneous characteristics of the built-up land.Among many techniques developed for the extraction of built-up land information,the index-based built-up index(IBI) was created based on three existing thematic indices rather than original multispectral bands.The use of the three thematic indices-soil-adjusted vegetation index(SAVI),modified normalized difference water index(MNDWI) and normalized difference built-up index(NDBI)-greatly help the delineation of built-up land features in remote sensing imagery,because these three indices represent three major landuse components,which are vegetation,water and built-up land,respectively.Therefore,the IBI can significantly enhance built-up land information while suppressing background noise.Consequently,the built-up land can be effectively extracted from the IBI image with high accuracy.In order to quicken image processing,this built-up extraction technique has been programmed to form an easy-use module using the ER Mapper scripting language.The module was further integrated in the ER Mapper package by adding a button to the manual bar.This allows users to automatically perform the extraction procedure with high accuracy just in a few minutes.

Key words: index-based built-up index(IBI), remote sensing, image processing