Performance of widely used feature detection techniques for co-registration of planetary images

Kurzfassung

Image co-registration, which is an important preprocessing step in planetary remote sensing for various scientific use-cases, is the alignment of images of the same scene acquired at a different time, from different viewpoints or using different sensors. Automated techniques for image alignment can be based on correlating points in the images. This creates the need for accurate and precise feature detection and matching. In feature detection, first distinctive keypoints within an image, such as corners, lines, edges or blobs are identified. Many methods are generating a feature descriptor for each keypoint, which forms a numerical representation of the keypoint that describes the local image characteristics at the point. Finally, those descripted keypoints are matched to create a correspondence between homologous local features in a second image. While recent feature detection and matching methods have been widely tested on terrestrial images, we aim to test their performance for planetary images. In the context of this work, the image data comes from the High Resolution Stereo Camera (HRSC) on ESA’s MarsExpress mission [1]. The images are orthorectified with the global DEM from Mars Orbiter Laser Altimeter (MOLA, [2]) data. This ensures a fairly good alignment between the images of the HRSC Nadir, Stereo and Photometry images while still typically including pixel offsets of a few pixels.