Abstract
Small satellites have limited payload and their attitudes are sometimes difficult to determine from the limited onboard sensors alone. Wrong attitudes lead to inaccurate map projections and measurements that require post-processing correction. In this study, we propose an automated and robust scheme that derives the satellite attitude from its observation images and known satellite position by matching land features from an observed image and from well-registered base-map images. The scheme combines computer vision algorithms (i.e., feature detection, and robust optimization) and geometrical constraints of the satellite observation. Applying the proposed method to UNIFORM-1 observations, which is a 50 kg class small satellite, satellite attitudes were determined with an accuracy of 0.02°, comparable to that of star trackers, if the satellite position is accurately determined. Map-projected images can be generated based on the accurate attitudes. Errors in the satellite position can add systematic errors to derived attitudes. The proposed scheme focuses on determining satellite attitude with feature detection algorithms applying to raw satellite images, unlike image registration studies which register already map-projected images. By delivering accurate attitude determination and map projection, the proposed method can improve the image geometries of small satellites, and thus reveal fine-scale information about the Earth.
Highlights
In recent years, more and more small satellites have been launched and operated for various purposes [1]
We identify land feature locations in the satellite image by comparing feature points on Earth which can be extracted from base map images containing recognized geographical information
random sample consensus (RANSAC)), M-estimator SAC (MSAC), MLESAC, and progressive SAC (PROSAC), were examined with Kanto, Yosemite, and Kyushu, and they all selected the same sets of inlier pairs in all the three cases
Summary
More and more small satellites have been launched and operated for various purposes [1]. Without an STT, the uncertainty in attitude determination can be several degrees as reported on UNIFORM-1 [3], and RIGING-2 [4] Such a large uncertainty in satellite attitude determination makes map measurements using the satellite observation images almost useless because the registration error in map projections can reach 50–100 km from. The accuracy of the map projection was assessed by the registration errors, defined as the differences between the detected feature points. As the VIS and OLI point in the projected satellite VIS image using the estimated rotation matrix. Feature detection with SURF outputs the locations to sub-pixel accuracy, regardless of scale differences. The mean displacements of feature points, which indicate the absolute geometric accuracy of registration, detection with SURF outputs the locations to sub-pixel accuracy, regardless of scale differences
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