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Describing the problem-solving strategies of expert image interpreters using graphical knowledge elicitation methods

机译：使用图形化知识启发方法描述专家图像解释器的问题解决策略

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Developing insights from remote sensing data requires a human-in-the-loop. The earliest examples are of air photo interpretation experts working with minimal tools. Today, experts in image interpretation rely heavily on the use of computer displays to assist them in insight generation. The goal of this current research is to develop a method for systematically describing the problem-solving tasks completed by experts when faced with the challenge of mapping land cover changes from remote sensing imagery. Knowledge elicitation (KE) methods are used to derive the interpretation processes of experts. Two KE methods, self-constructed external representation (SER) and think-aloud protocol (TAP), are used to develop a descriptive model of expert image analysis. A set of cognitive tasks are identified using qualitative coding of the SER, TAP, and reflection interviews. From these codes, a generalized interpretation model is generated to reflect the use of TimeSync, a visual interpretation tool, for land cover change mapping. Two findings emerge from this work. First, the use of SER and TAP in conjunction with qualitative coding is a useful means for generating the general interpretation model. Second, the interpretation of land cover change from remote sensing imagery consists of several cognitive subtasks that support a process of identification and signification process. The main contribution of this work is toward the development of a useful framework for using graphical KE methods in the context of visual work.
机译：从遥感数据中获得洞察力需要一个人在环。最早的例子是使用最少工具的航空照片解译专家。如今，图像解释专家在很大程度上依赖于计算机显示器的使用，以帮助他们产生见解。这项当前研究的目标是开发一种方法，以系统地描述专家在面对绘制遥感影像的土地覆盖变化图所面临的挑战时完成的问题解决任务。知识启发（KE）方法用于导出专家的解释过程。自建外部表示（SER）和自言自语协议（TAP）这两种KE方法用于开发专家图像分析的描述性模型。使用SER，TAP和反思访谈的定性编码可以识别出一系列认知任务。从这些代码中，将生成一个通用的解释模型，以反映TimeSync（一种视觉解释工具）用于土地覆被变化制图的使用。这项工作有两个发现。首先，将SER和TAP与定性编码结合使用是生成通用解释模型的有用手段。其次，对遥感影像中土地覆盖变化的解释包括几个认知子任务，这些任务支持识别和指示过程。这项工作的主要贡献在于开发了一个有用的框架，用于在视觉工作的上下文中使用图形化KE方法。

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