Data fusion of high-resolution satellite images and airborne laser scanning data for building detection in urban environment

摘要

High-resolution satellite imagery has become widely available recently and it enables urban remote sensing to not only classify land-use, but also map the details in urban environment. However, due to high object density and scene complexity, normally it is extremely difficult to automatically extract urban objects solely based on images. This paper describes our approach to detect buildings by fusing high-resolution IKONOS satellite images and airborne laser scanning data. With the high spatial resolution, rich spectral signature of IKONOS images and the very accurate positioning information of laser data, our data fusion methods show an efficient way to exploit the complementary characteristics of these two kinds of dataset for the purpose of building detection. In order to simplify the complexity of processing, a top to down strategy is generally applied to extract features of objects from coarsely to finely, and multiple cues are also derived and fused at different processing levels. The paper describes the developed framework and experimental results in detail, and also discusses both the advantage and deficiencies of the approach.