Automatic building change detection through adaptive local textural features and sequential background removal

Abstract

In this paper, we present a new framework for building change detection from monocular aerial imagery that automatically predicts building candidates based on adaptive local textural features with successive background removal. An adaptive local entropy feature is developed based on quadratic regression and Random Sample Consensus (RANSAC) for extracting potential building candidates. Then a majority voting aggregation strategy is employed to accurately estimate shadow direction associated with building objects to aid in reducing false positives in the detected building candidates. A ground plane estimation method is proposed to distinguish building and non-building objects that share similar textural features. Finally, to better estimate changes in building area, a double convex hull based morphological merging technique is introduced. The evaluation results of the proposed framework performed on three-band (RGB) aerial images indicate its capability to successfully detect building changes in urban, suburban, and rural areas.