Remote Sensing Scene Classification

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Remote sensing, the science of aerial or satellite data capture of the earth, provides crucial information for such important endeavours as smart agricultural practices, shipping in northern waters, and monitoring of greenhouse gases. Vast amounts of data are generated, and computer-based algorithms to interpret the data are urgently needed. For example, using the Canadian RADARSAT-II satellite, Canadian Ice Service (CIS) personnel manually interpret 4000 scenes annually, providing ice type maps over huge (500km by 500km) regions that infer ice thickness and strength to facilitate decision support systems for shipping and icebreaking. To save time and money and to reduce human bias, there is a demand for machine algorithms that can analyze this imagery. My particular interest is the automated interpretation of this radar-based sea ice imagery, considered the "holy grail" of remote sensing problems, a challenge due to the sensor characteristics and environment variability that confuse the ice types.