Advanced Intelligent Computer Vision for Remote Sensing Scene Interpretation

遥感，航空或卫星数据捕获地球的科学，提供了陆地，海洋和大气层的关键信息。 作为世界经济中不断增长的一部分，遥感市场预计在2017年将达到近100亿美元。 考虑到现在产生的大量遥感数据以及未来的增长，迫切需要自动化算法来增加吞吐量，减少周转时间，节省资金，并进行常规监测，而专家必须接受培训，以了解和调查科学问题并领导未来的任务。我的研究重点是设计和实施自动化算法，利用卫星数据监测地球的海洋。 关键图像由世界级的加拿大卫星RADARSAT-2以及2018年发射日期后由称为RADARSAT星座使命（RCM）的三颗加拿大卫星组成。 这些卫星的优先事项是监测加拿大境内和周围的冰患沃茨，加拿大冰务局是负责制作所需冰图的政府机构。 CIS人员每年人工解读10，000个场景，提供巨大（500公里乘500公里）区域的冰类型和范围，以推断冰的厚度，范围和强度，以促进航运和破冰的决策支持系统。我开发的人工智能算法可以像人眼一样有效地阅读和解释图像，但我的算法提供的细节远远超过人类操作员。 我的算法可以确定一个特定的像素是冰还是水，并且可以进一步将其分解为确定冰的类型（例如，年轻或年老的冰。 海冰的类型和范围是船舶导航的关键信息，船长不想在没有这些信息的情况下进入结冰的沃茨，我的算法可以将这些信息传递给他们。 可以监测湖冰的冻结和融化，以确定湖上冰的总体浓度，这是了解气候变化影响的关键信息。 此外，我正在开发能够在雷达图像中检测石油泄漏的算法。 一些船只非法向海洋倾倒舱底油，这留下了必须通过卫星图像快速识别的石油痕迹。 我的算法会找到这些石油泄漏，这样犯罪者就可以被逮捕。其他北方国家可能会有兴趣购买根据所提出的算法绘制的地图。我在自动图像解释方面的研究已经并将继续成功地应用于其他领域的自动化，如医学成像，3D重建，体育分析和视频分析，同时培训学生计算机视觉，以解决这些先进的智能系统问题，并在行业中发挥领导作用。

Remote sensing, the science of aerial or satellite data capture of the earth, provides crucial information across land, oceans, and atmosphere. As a growing part of the world economy, the remote sensing market is expected to reach nearly $10 billion in 2017. Given the vast amount of remote sensing data being produced now and increasing in the future, automated algorithms are urgently needed to increase throughput, reduce turnaround time, save money, and perform routine monitoring while experts must be trained to understand and investigate scientific questions and lead future missions.******My research focuses on the design and implementation of automated algorithms to monitor the earth's oceans using satellite data. The key images are produced by a world-class Canadian satellite, RADARSAT-2 and, after the launch date in 2018, by a group of three Canadian satellites called the RADARSAT Constellation Mission (RCM). The priority of these satellites is the monitoring of ice-infested waters in and around Canada and the Canadian Ice Service (CIS) is the government agency tasked with producing the required ice maps. CIS personnel manually interpret 10,000 scenes annually, providing ice typing and extents over huge (500km by 500km) regions in order to infer ice thickness, extent, and strength to facilitate the decision support systems for shipping and ice breaking.******I develop artificial intelligence algorithms that can read and interpret the imagery as effectively as the human eye, but my algorithms provide far more detail than a human operator. My algorithms can determine whether a particular pixel is ice or water, and can further break this down into determining the ice type (e.g., young or old ice). Sea ice types and extents are crucial information for ship navigation and ship captains do not want to enter ice-infested waters without this information my algorithms can deliver this information to them. Lake ice can be monitored for freeze up and melt to establish the overall ice concentration on the lake, and this is crucial information for understanding the impact of climate change. Further, I am developing algorithms that are able to detect oil spills in the radar-based imagery. Some ships illegally dump bilge oil into the ocean and this leaves a trail of oil that must be identified by satellite imagery quickly. My algorithms will seek to find these oil spills so that the perpetrators can be apprehended.******Other northern nations would be interested in purchasing maps derived from the proposed algorithm. My research in automated image interpretation to solve these problems has also been and will continue to be successfully applied for automation to other domains such as medical imaging, 3d reconstruction, sports analytics, and video analytics, while simultaneously training students in computer vision to tackle these advanced intelligent systems problems and gain leadership roles in industry.