Graduate Student Profiles

Raechel Bianchetti

Raechel BianchettiRaechel Bianchetti will graduate from Penn State with a Ph.D in Geography in August 2014, and she has already accepted a tenure-track position in the Geography Department at Michigan State University.  As a graduate affiliate of the GeoVISTA Center, Raechel contributed to the SymbolStore project. Her research interests center on remote sensing and physical geography, cognitive GIScience, and historical geography. Raechel was also a member of the Human Factors in GIScience Lab.

Raechel successfully defended her thesis, Looking Back to Inform the Future: The role of cognition in forest disturbance characterization from remote sensing imagery, in May of 2014. Raechel’s dissertation work, which was supported by a Doctoral Dissertation Research Improvement grant from the National Science Foundation, is rooted in her interdisciplinary educational training. It draws from theories in cognitive science, Human Computer Interaction, Forest Ecology, Remote Sensing, Geovisual Analytics and the broader GIScience. The goal of this research is to illuminate cognitive aspects of the image analysis process including elements of human perception and human reasoning abilities to inform visualization techniques and tools. The knowledge gained from Raechel’s research will be used to implement novel Geovisual Analytics methods to support the image analysis process. The primary objectives of this research are:

  1. Identify the cognitive tasks that image analysts perform during image analysis
  2. Assess the use of air photo interpretation by image analysts for the identification of subtle differences in relatively homogenous landscapes
  3. Design a set of interactive visual aids to support the identification and interpretation of forest disturbances in imagery using geovisual methods and tools

More About Raechel's Research

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.

Short paper: Considering Visual Perception and Cognition in the An alysis of Remotely Sensed Images

Poster Abstract: Deepening our understanding of cognitive aspects of image analysis: Relationships between medical image processing and earth observation

Raechel's Full Thesis: Looking Back to Inform the Future: The role of cognition in forest disturbance characterization from remote sensing imagery

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