Multi-sensor fusion for classification and change-detection in remote-sensed imagery

A single remote-sensed image provides a snap shot of a scene at a particular instance in time. The measurement process, and hence the grey-levels of the final image, is dependent on the properties of the scene, the sensor and the sensing conditions. Combining information from multiple images, possibly at different times or with different sensors, enables further properties of the scene to be deduced. The motivation for this is to either improve classification accuracy of the scene, or else to quantify temporal variations in either shape or attributes of the scene. This latter problem is termed change detection. The approach described is based on feature-based correspondence of regions in the two input images. Images are processed from pixel data to be stored in a vector database, via a segmentation algorithm. Single-image classification and attribute extraction may be carried out prior to combination