Developing a deep scene parser for UAV-acquired images

视线是视觉分析系统开发和集成领域的领先者，其核心VtiS(视觉、遥测、情报系统)平台旨在扩大无人机和国防、搜索和救援以及企业情报领域的态势感知。泰勒博士是深度学习领域的专家，深度学习是一种使用多层处理自动从数据中提取有用的表示和抽象的方法。我们共同提出了一种无人机图像数据场景解析器的开发方案。场景分析是将每个像素分配到一个类别，例如，道路、建筑、人和汽车。它使人类观察者和机器都能够识别场景中存在的对象，推理它们之间的关系，并制定适当的反应。场景解析器将为VtiS平台提供有价值的分析组件，支持Sightline在无人机系统市场的增长。为了处理海量的数据，我们将利用高性能计算的进步，特别是图形处理器(GPU)。场景分析中的主要算法挑战是标记像素所需的信息通常来自距离较远的像素以及它们的标签。深度学习提出了一种整合本地和全球信息的方法。它在非空中场景解析方面取得了一些适度的成功，例如，标记街道场景。然而，这些工作都没有着眼于航空图像，这涉及到额外的研究挑战，例如集成多个传感器类型，以及利用捕获图像之间的时间相关性。我们希望我们的研究能够立即在搜救领域得到应用。从长远来看，我们预计这项研究的结果也将影响精准农业。

Sightline is a leader in visual analytics systems development and integration - its core VtiS (Vision, Telemetry, Intelligence System) platform is designed to broaden situational awareness across UAV & defence, search and rescue, and enterprise intelligence. Dr. Taylor is an expert in the area of Deep Learning: methods that automatically extract useful representations and abstractions from data using multiple layers of processing. Together we propose the development of a scene parser for UAV-acquired image data. Scene parsing is the assignment of each pixel to a category, for example, roads, buildings, people and cars. It enables both human observers and machines to identify what objects exist in a scene, reason about their relationships, and develop appropriate responses. The scene parser will provide a valuable analytical component to the VtiS platform supporting Sightline's growth in the Unmanned Aircraft Systems market. To tackle the sheer amount of data to be processed, we will leverage advances in high-performance computing, specifically Graphics Processing Units (GPUs). The major algorithmic challenge in scene parsing is that the information necessary for labeling a pixel often comes from distant pixels as well as their labels. Deep Learning proposes a way to integrate both local and global information. It has had some moderate successes in non-aerial scene parsing, for example, labeling street scenes. However, none of this work has looked at aerial images which involve additional research challenges such as integration of multiple sensor types, and exploiting the temporal dependence among captured images. We expect our research to lead to immediate applications in the area of search and rescue. In the longer term, we expect the outcomes of this research to also impact precision agriculture.