A novel productive framework for point‐based feature matching of oblique aircraft and UAV imagery is presented. The proposed framework makes use of the powerful AKAZE descriptor for feature extraction and an iterative scheme is developed to construct as many tentative matches as possible. During the iterations, cross checks, together with Lowe’s nearest‐next distance ratio test, are used to filter erroneous matches. In order to extract putative matches from the tentative matches, three robust approaches, including graph‐cut RANSAC, are evaluated along with the epipolar constraint enforced between the two datasets. The developed framework was validated using the ISPRS image orientation benchmark dataset and yielded successful results in terms of matching precision, even for some difficult cases. The results also outperformed the results of previously developed approaches in the same context.
Abstract. EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites), an intergovernmental organisation founded in 1986, supplies weather and climate-related satellite data, images and products throughout the year for EU Member States and other users worldwide. The optical Earth Observation satellites launched and operated by EUMETSAT, both current and planned ones, have different spatial, spectral and temporal resolutions; sensor models and acquisition geometries. While the number and the diversity of the satellite missions increase, the requirement of novel methods and up-to-date reference data for geometric accuracy assessment of the imagery also grows. This paper aims at reporting the results of a study investigating the availability for suitable satellite imagery to be employed as reference data for the geometric quality assessment (GQA) of MSG SEVIRI Level 1.5 image products. The reference datasets need to have superior spatial resolution, wide global coverage, and spectral compatibility with respect to the SEVIRI sensor, which has 12 spectral bands with 1 km and 3 km spatial resolutions. The SEVIRI sensor works with whiskbroom principle at a geostationary orbit and collects data at 5 minutes (rapid scan) and 15-minutes (full scan) intervals. Although preliminary investigations on reference data were performed by using images of different satellite sensors during the study, in-depth investigations were performed with MERIS global image mosaic and Landsat imagery. The progress and different problems observed in the images are reported here.
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