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Detecting Transient Surface Features with Dynamic Landmarking
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| Our goal is to dynamically and autonomously detect transient surface features, such as dust devil tracks or dark slope streaks on
Mars, from images. This goal is particularly timely given the announcement that recent surface changes (a new gully and 20 impact
craters) have been discovered on Mars (Malin et al., 2006). These critical discoveries tend to proceed slowly because scientists rely
primarily on manual examination of each image pair to identify any changes. The automated methods that do exist focus on per-pixel
changes, require full image registration, and are prone to generating false detections. We propose a major paradigm shift by
decoupling change detection from the individual image pixels. We will provide a landmark-based analysis that enables both easier
interpretation and the ability to quickly detect any changes. Dynamic landmarking can detect transient features without requiring
image registration, which represents a large step towards enabling the onboard use of this technology. Because the landmarks are
represented at an abstract level, it is possible to combine observations from different instruments at varying attitudes and under
different illumination conditions. No system currently exists to provide this level of sophistication in change detection for planetary
surface features; success in this area will greatly increase the number of surface chances that can be detected. We will demonstrate this
capability on overlapping images collected by the Mars Orbiter Camera (MOC) and Thermal Emission Imaging System (THEMIS),
two Mars imagers on different spacecraft. Dynamic landmarking first selects surface features that will function as landmarks in each
image. Landmarks may include craters, volcanoes, fissures, and so on, as well as the transient features we seek. Next, we extract
descriptive attributes for each landmark, such as size (surface area), shape, albedo, homogeneity, etc. Using these attributes, we can
automatically classify the discovered features into a set of known classes of interest--and mark any unclassified features as new,
potentially high-interest features. These landmarks provide a regional characterization of the area covered by the image that can be
used to better understand surface processes as well as to recognize when two images overlap. Finally, we can compare the landmark
sets discovered in two images to quickly detect any changes with high precision. One of the biggest benefits of this effort is the increased
productivity it will lend to science investigations, as compared with manual change detection. This work will lead directly to increased
science return from data that is already being collected. Specifically, it can increase our knowledge about other planetary surfaces, and
the dynamic processes present, to support future human and robotic exploration. |
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