Data dimension reduction for visual analytics: A case study of oil-in-water detection
Ottar L. Osen, Anete Vagale, Hao Henry Wang, Karina Hjelmervik, Halvor Schøyen · OCEANS 2017 – Anchorage · 2017
Many different sensors have been installed on board vessels, a new framework is urgently needed to form a Common Operational Picture (COP), to assist the on-board operations and onshore analysis. In this paper, based on the Visual Analytics framework, we present a spatiotemporal dimension reduction method based on a real world case of oil combat operation. With our prototype, we show that how dimension reduction of spatiotemporal data will improve visualisation and ease analysis.