Making sense of Raster chart: a computer vision approach

Adrian Mai; James Chao; Mitch Manzanares; Douglas Seth Lange

doi:10.1117/12.2618910

27 May 2022 Making sense of Raster chart: a computer vision approach

Adrian Mai, James Chao, Mitch Manzanares, Douglas Seth Lange

Proceedings Volume 12099, Geospatial Informatics XII; 120990A (2022) https://doi.org/10.1117/12.2618910
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Sea navigation and operations within areas of interest has been a major focus of naval research. Documents such as Raster Navigational Charts (RNC) that help with sea navigation tasks are critically important. A RNC is a copy of a navigational paper chart in image form. Therefore, RNC contains important information such as navigational channels, water depths, rocky areas etc. However, a RNC is hard to interpret by computers and even humans as it contains very dense information due to the different layers of drawings from the information mentioned above. In this paper, we introduce a reverse engineering approach using computer vision to extract features from the RNC image. We use optical character recognition to extract text features and templates matching for symbolic features. With the new approach, we show that RNC will become machine readable, and the features extracted can be used to draw tactical regions of interest.

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Citation Download Citation

Adrian Mai, James Chao, Mitch Manzanares, and Douglas Seth Lange "Making sense of Raster chart: a computer vision approach", Proc. SPIE 12099, Geospatial Informatics XII, 120990A (27 May 2022); https://doi.org/10.1117/12.2618910

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