Representation of predicted accuracy of 3d geospatial products and their subsequent fusion with other products

John Dolloff

doi:10.1117/12.2519059

13 May 2019 Representation of predicted accuracy of 3d geospatial products and their subsequent fusion with other products

John Dolloff

Author Affiliations +

Proceedings Volume 10992, Geospatial Informatics IX; 109920G (2019) https://doi.org/10.1117/12.2519059
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

This paper describes a method to represent the predicted accuracy of an arbitrary 3d geospatial product from a specific type or class of products; for example, the class of 3d point clouds generated from EO imagery by vendor “abc” within date range “xyz”. The predicted accuracy is based on accuracy assessments of previous products from the same type or class of products; in particular, based on corresponding sample statistics of geolocation error computed using groundtruth or surveyed geolocations. The representation of predicted accuracy is theoretically rigorous, flexible, and practical and is based on the underlying concepts of Mixed Gaussian Random Fields (MGRF). It also allows for a representation of predicted accuracy that can vary over the product, allowing for increased geolocation uncertainty for a priori“problem areas” in the product. The MGRF-based approach for the representation of predicted accuracy is particularly applicable to 3d geospatial products that do not have product-specific predicted accuracies generated with the product itself. This is the typical situation, particularly for commodities-based geospatial products. The paper also describes a method for the near-optimal adjustment of a geospatial product based on its predicted accuracy and its fusion with other products.

Conference Presentation

Citation Download Citation

John Dolloff "Representation of predicted accuracy of 3d geospatial products and their subsequent fusion with other products", Proc. SPIE 10992, Geospatial Informatics IX, 109920G (13 May 2019); https://doi.org/10.1117/12.2519059

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