Design of adaptive spatiotemporal information and control systems

当今遥感系统的数据收集、处理和联网能力急剧增加，使遥感坚定地走上了成为一项普及技术的道路，以各种方式影响社会。现在迫切需要的是强大的目标检测和场景解释方法，能够自动处理和解释这些丰富的信息。本项目建立在学习控制和模式识别/图像处理的协同作用上，提出了主要在环境应用中遇到的复杂动态时空系统的建模、监测和控制的创新技术。总体目标是开发一种集成设计方法，解决与大量多维、异构传感器数据的自适应处理相关的问题。更具体的目标包括研究设计和优化生物启发的专注视觉算法的理论和方法基础，以实现异构、稀疏遥感图像的时空显著性，以及开发新的迭代学习方法来解决不适定目标检测和分类问题。方法方法是在地面真值参考信息稀缺的情况下定义的，这些信息很难获得，而且成本很高。它涉及将基于核的方法和相关机器学习范式（确定性学习和迭代学习）集成在一起的混合技术的发展，用于时变，不确定系统的监测和控制应用。这个跨学科的项目将有助于培养各个层次的研究人员，通过与工业伙伴的合作，其培训计划将大大加强。除了对新科学知识的贡献外，这项研究的结果将直接造福加拿大社会，帮助满足对复杂信息技术的需求，以有效地监测和管理加拿大广阔领土上的自然资源。

Steep increases in the data gathering, processing and networking capabilities of today's remote sensing systems have put remote sensing firmly on the path to become a pervasive technology, affecting society in a variety of ways. What is now critically needed are powerful object detection and scene interpretation methods amenable to automated processing and interpretation of this wealth of information. This project builds on the synergy of learning control and pattern recognition/image processing to propose innovative techniques for modeling, monitoring, and control of complex, dynamic spatiotemporal systems as encountered mainly in environmental applications. The overall objective is to develop an integrated design methodology that addresses issues relating to the adaptive processing of large quantities of multi-dimensional, heterogeneous sensor data. More specific objectives include research on theoretical and methodological foundations for the design and optimization of biologically-inspired attentive vision algorithms for spatiotemporal saliency in heterogeneous, sparse remote sensing imagery, and the development of novel iterative learning methods to solve ill-posed object detection and classification problems. The methodological approach has been defined in the context of the scarcity of ground truth reference information, which can be difficult and costly to acquire. It involves the development of hybrid technologies integrating kernel-based methods and pertinent machine learning paradigms (deterministic learning and iterative learning) for time-variable, uncertain system monitoring and control applications. This interdisciplinary project will help develop research personnel at all levels, with its training program significantly enhanced by the opportunities arising from the collaboration with industrial partners. Apart from the contribution to new scientific knowledge, the results of this research will directly benefit Canadian society, helping to meet the need for sophisticated information technologies to efficiently monitor and manage natural resources in Canada's vast territory.