A study of multi-sensor satellite image indexing

In the context of earth observation, different sensors have been used to acquire satellite images and it becomes a research topic about how to analyse and use multi-sensor images. In this paper, we carry out a study of multi-sensor satellite image indexing. The goal is to study which kind of satellite image provides more information for classification. To this end, we prepared four datasets covering four typical cities. Each dataset consists of three kinds of images: multispectral and panchromatic images from WoldView-2, Synthetic Aperture Radar (SAR) images from TerraSAR-X satellite. Image indexing is performed at patch level with the same feature extraction method. The indexing is carried out using an active learning system we developed before. A series of independent and joint indexing by combining the features have been performed. Through this study, we found that the indexing accuracy on SAR images is the worst. By contrast, the joint indexing by concatenating the features computed from each kind of image could provide best accuracy. Thus, we conclude that combing information from multi-sensor images could achieve better results than using each kind of image independently.