Urban Change Detection under Large View and Illumination Variations
视角和光照显著变化时的变化检测方法研究

This paper intends to explore the state of the art computer vision techniques to detect urban changes from bi-temporal very high resolution (VHR) remote sensing images. The underlying principle of this work is that most real urban changes usually involve clustered line segment changes. Several major problems are investigated when the view angle and illumination condition undergo large variations. In particular, a method is proposed to extract roof regions of unchanged tall buildings by matching both image point groups and image regions. In addition, by considering the geometrical constraints, the concept of change blindness region is introduced to remove the image changes related to unchanged tall buildings. Experiments with real remote sensing images show that the proposed approach still performs well though the image pairs undergo significant variations of viewing angles and illumination conditions.