Automatic Building Change Detection and Open Space Area Extraction in urban areas

Abstract

Automatic change detection and open space area extraction in urban environment is one of the crucial components towards the efficient updating of Geographic Information System (GIS), government decision-making, urban land management and planning. Original Morphological Building Index (MBI) can extract interest building features for multitemporal high-resolution satellite image but this approach wrongly classified as buildings. In this paper, jointly approach of modified MBI, Normalized Different Vegetation Index (NDVI) and Entropy is developed for identifying low quality satellite images over different years. Then, matching-based change rule is applied to obtain changes area of urban region. The proposed method is insensitive to the geometrical differences of buildings caused by different imaging conditions and is able to significantly reduce false alarms and also achieves much improved detection accuracy and overall performance. The effectiveness of the method is validated by comparing with MBIbased Change Vector Analysis (CVA) and Multivariate alteration detection (MAD) transformation.