Doctoral Dissertation Research: Assessing the Cognitive Aspects of Satellite Image Interpretation

遥感图像是环境科学、环境管理和相关领域广泛研究活动的重要输入。例如，分析人员使用多光谱图像来检测和监测森林干扰，分析栖息地丧失和破碎化，并评估物种多样性。最近遥感研究的很大一部分是针对图像分析的自动化。然而，人类分析人员的作用同样重要，因为目前还没有全自动图像分析系统。通过将人类专业知识与计算机处理速度和一致性相结合的视觉分析方法的应用，有可能提高图像派生信息的准确性、精确性和任务相关性。这种耦合需要更全面地理解人类分析师在使用图像时的感知和推理过程。这个博士论文研究项目将调查认知任务和基本视觉刺激，用于森林管理应用领域内的航空图像解释。为了对森林分析人员所使用的高级思维过程和低级视觉线索产生认识，博士生将使用两种认知方法。首先，她将使用应用认知任务分析，一种基于半结构化访谈和图表活动的方法，来揭示分析师在图像分析任务中使用的知识结构和认知技能。其次，她将进行受控的认知实验，以识别被认为对遥感图像的视觉解释最重要的视觉线索。利用在这两个研究阶段获得的知识，将开发一套可视化分析工具，以支持对森林干扰的遥感图像进行半自动分析。目前遥感研究的趋势集中在发展和改进自动化过程，以处理日益增加的图像数据量。这些研究工作往往没有考虑到人类操作者在过程中的重要性，也没有考虑到人类指导的分析过程所能提供的好处。该项目将阐明图像分析背后的认知过程，包括高层次的思维过程和对图像的低级视觉感知。考虑图像解释过程的两个方面，将提供一个机会来澄清图像分析这两个方面之间的联系，并将为视觉分析方法的发展提供直接输入，这些方法将人类专业知识与计算方法以富有成效的方式联系起来。因此，该项目将有助于开展一系列活动，包括森林科学和管理实践、地理空间情报分析和图像分析培训。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立研究生涯。

Remotely sensed imagery is a critical input to a wide range of research activities in environmental science, environmental management, and related domains. For example, analysts use multi-spectral imagery to detect and monitor forest disturbances, analyze habitat loss and fragmentation, and assess species diversity. A large proportion of recent research in remote sensing has been directed to automation of image analysis. The role of the human analysts is equally important to consider, however, because no fully automatic image analysis system currently exists. Through application of a visual analytics approach that couples human expertise with computer processing speed and consistency, it may be possible to improve accuracy, precision, and task-relevance of image-derived information. This coupling requires a more comprehensive understanding of the human analysts' perceptual and reasoning processes when they use the imagery. This doctoral dissertation research project will investigate cognitive tasks and fundamental visual stimuli used in the interpretation of aerial imagery within the application domain of forest management. To create an awareness of both high-level thought processes as well as low-level visual cues that are used by forest analysts, the doctoral student will use two cognitive methods. First, she will use applied cognitive task analysis, a method based on semi-structured interviews and diagramming activities, to uncover the knowledge structures and cognitive skills analysts use during the image analysis task. Second, she will conduct controlled cognitive experiments to identify visual cues deemed most important for visual interpretation of remotely sensed imagery. Using the knowledge gained during these two phases of research, a set of visual analytics tools will be developed to support semi-automated analysis of remotely sensed images for forest disturbances.The current trend in remote sensing research focuses on the development and improvement of automated processes for addressing the increasing volumes of imagery data. These research efforts often fail to consider the importance of human operators in the process, and they do not consider the benefits that human-guided analytic processes can provide. This project will illuminate the cognitive processes that underlie image analysis, including both high-level thought processes as well as the low-level visual perception of imagery. Considering both facets of the image-interpretation process will provide an opportunity to clarify the links between these two aspects of image analysis and will provide direct input to development of visual analytic methods that connect human expertise with computational methods in productive ways. The project therefore will be useful for a range of activities, including forest science and management practices, geospatial intelligence analysis, and image analyst training. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.