Robust object detection using context and component information

航空/空间遥控器目前获取了大量的光电监视数据，具有多光谱、多时相、地理参考数据，并显著增加了空间、光谱！和辐射分辨率。高分辨率的航空图像可以提取关于地面物体的3D信息。对于大规模图像库来说，人工处理这些数据以进行图像配准和融合、环境变化识别和目标检测可能既耗时又不一致。由于多媒体采集、存储、传输、可视化和交互技术的快速发展，民用数字图书馆也存在类似的问题，这些数字图书馆在多媒体和数据库中包含了海量的可视数据。

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.