Remote Sensing Scene Classification

遥感是一门利用航空或卫星获取地球数据的科学，它为智能农业实践、北方水域航运和温室气体监测等重要工作提供了关键信息。产生了大量的数据，迫切需要基于计算机的算法来解释这些数据。例如，使用加拿大RADARSAT-II卫星，加拿大冰局（CIS）人员每年手动解释4000个场景，提供巨大（500公里乘500公里）区域的冰型地图，推断冰的厚度和强度，以促进航运和破冰的决策支持系统。为了节省时间和金钱，减少人为偏见，需要能够分析这些图像的机器算法。我特别感兴趣的是这种基于雷达的海冰图像的自动解释，这被认为是遥感问题的“圣杯”，由于传感器的特性和环境的变化，混淆了冰的类型，这是一个挑战。

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.