Robust object detection using context and component information

目前，航空/空间遥感器获得了大量的数据，用于多光谱、多时间、地理参考数据和显著增加的空间、光谱的光电监视！还有辐射分辨率。高分辨率航拍图像可以提取地面物体的三维信息。对于大规模的图像库，手动处理这些数据进行图像配准与融合、环境变化识别和目标检测可能耗时且不一致。随着多媒体采集、存储、传输、可视化和交互技术的快速发展，民用数字图书馆中也存在着类似的问题，多媒体和数据库中包含着大量的可视化数据。

Aerial/space remote sensors acquire overwhelming amount of data at the present time for opto-electronic surveillance with multi-spectrum, multi-temporal, georeference data and significantly increased spatial, spectra! and radiometric resolutions. High-resolution aerial images permit to extract 3D information on the grounded objects. Manually processing those data for image registration and fusion, environmental change identification and object detection could be time-consuming and inconsistent for the large-scale image bank. The similar problematic exists in the civil digital libraries, which contain huge amount of visual data in the multimedia and database, thanks to rapid growth in the multimedia acquisition, storage, transmission, visualization, and interaction technologies.