视角和光照显著变化时的变化检测方法研究

Urban Change Detection under Large View and Illumination Variations

探讨使用计算机视觉的最新方法来解决基于两幅高空间分辨率光学遥感图像的城市变化检测问题.基本原理是通过提取聚类出现的变化直线段群来提取城市变化,重点研究了拍摄视角和光照条件显著变化时的几个主要问题.提出了一种基于多种类型图像特征的匹配方法来提取无变化建筑的顶部区域,结合几何约束引入了变化盲区的概念以处理高层建筑在不同视角和光照下的图像不同现象.使用真实遥感图像进行实验,在视角和光照显著变化时仍可取得满意的变化检测结果.

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.