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- W2946025125 abstract "A central theme in the field of photogrammetry is the improvement of geo-spatial accuracy. However, the accurate geo-localization for low-lost UAV systems that are equipped with cheap and light GNSS/INS remains an open problem. In contrast, aerial imagery acquired by manned aircrafts usually has much higher geo-referencing accuracy of up to centimeter level. Additionally, aerial imagery provides complimentary scene representations from a larger scale and a different perspective compared to UAV imagery. Thus, the combination of the data acquired by UAVs and manned aircrafts could contribute to more comprehensive representations of the scenes of interest. To enhance utilization of such information, the implicit image semantic information needs to be jointly interpreted and analyzed.The primary goal of this thesis is to jointly exploit UAV and aerial imagery to enhance visual and spatial understanding of the scene, more concretely, to extract meaningful image semantics that have high geo-spatial accuracy. Thesis describes the efforts we have made towards this goal, and our contributions are mainly in three areas: 1.To improve the geo-spatial accuracy of UAV imagery by a pixel-level co-registration of UAV imagery and aerial imagery, a novel image feature matching algorithm for UAV and aerial image pairs is proposed, which is also applicable for heterogeneous images that have large differences in scale, rotation and appearance.2. Since supervised learning-based semantic image segmentation tasks require large amount of training data, which often costs intensive manual labor, we propose a pipeline that helps create image dataset with semantic level annotations using label transfer. 3. We leverage the image semantic information, particularly building segments such as roofs and facades, to generate true building footprints (excluding roof overhangs) with decimeter-level accuracy as well as 3D building models of LoD1." @default.
- W2946025125 created "2019-05-29" @default.
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- W2946025125 date "2019-01-01" @default.
- W2946025125 modified "2023-09-24" @default.
- W2946025125 title "Semantic Information Extraction from UAV Imagery and Aerial Imagery" @default.
- W2946025125 hasPublicationYear "2019" @default.
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