An Effective Morphological Index in Automatic Recognition of Built-up Area Suitable for High Spatial Resolution Images as ALOS and SPOT Data

Building detection from remote sensed images is the main technique to monitor economic or environmental development of an area. Advanced Land Observing Satellite (ALOS) and SPOT data are reliable sources due to the limitation of weather, position, time, and other practical reasons. However, to the best of our knowledge, algorithms proposed in the identification of buildings mostly aim only at images with very high spatial resolution or high spectral resolution. There are few algorithms for detecting buildings from ALOS and SPOT data. A built-up detection index (BDI) is proposed in this paper to automatically identify buildings from images with 10 meters resolution. It synthesizes morphological theory and normalized differential vegetation index (NDVI) to enhance buildings by suppressing vegetation. Four images of ALOS and SPOT are used to verify the efficiency, stability and accuracy of BDI Experiments show that BDI is suitable to detect buildings from 10 meters resolution with reliable accuracy.