Safe spacecraft landing on planetary and small body surfaces is of primary concern. Estimation of landing risk is a critical task when evaluating and certifying potential landing sites. Such analyses require the detection and mapping of all potential landing hazards such as rocks and boulders, craters, slopes, and terrain roughness.
The availability of very high-resolution orbital imagery from the HiRISE camera onboard the Mars Reconnaissance Orbiter, and from the Narrow Angle Camera (NAC) onboard the Lunar Reconnaissance Orbiter, makes it possible to detect and map boulders automatically. Computer vision techniques have been applied to implement algorithms that detect rocks from images. At NAC nominal resolution, it is possible to fully resolve boulders 2.5 m or larger, and detect boulders as small as 1.2 m. With knowledge of the Sun angles, the shadows detected from the images are used to generate rock models describing the location, width, and height of boulders. A mapping toolkit computes statistical plots and maps of rock density and abundance.
These algorithms are tailored to the lunar environment, including two major adaptations: automatic handling of shadow contrast variations and image blur, and a capability to distinguish small fresh craters (which cast shadows as well) from large boulders. The algorithms run on desktop computers and can process a NAC image in 30 minutes with multiple diagnostics output.
All current and future landed missions would benefit from detailed analyses of potential landing sites, provided orbital imagery with sub-meter resolution is available. If not available, hazard and avoidance systems are needed. The rock detection algorithms described were originally designed for such a task and would be readily applicable.
This work was done by Andres Huertas and Yang Cheng of Caltech for NASA’s Jet Propulsion Laboratory.
The software used in this innovation is available for commercial licensing. Please contact Dan Broderick at
This Brief includes a Technical Support Package (TSP).
Automatic Lunar Rock Detection and Mapping
(reference NPO-48104) is currently available for download from the TSP library.
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Overview
The document titled "Technical Support Package for Automatic Lunar Rock Detection and Mapping" outlines the methodologies and technologies developed for the automated detection and mapping of lunar rocks, primarily focusing on the work of researchers Andres Huertas and Yang Cheng at the Jet Propulsion Laboratory (JPL). It serves as a resource for understanding the advancements in lunar geology and the applications of these technologies in space exploration.
The document begins with an introduction to the project, detailing the primary data sources and the algorithms used for rock detection. It emphasizes the importance of high-resolution imagery, specifically mentioning the need for images with a ground sample distance (GSD) better than 1 meter. The document also discusses the optimal sun angles for imaging, recommending incidence angles between 30 and 70 degrees to enhance the visibility of rocks and craters.
A significant portion of the document addresses the limitations and issues encountered in rock mapping. It notes that while the imagery can tolerate some blur, low contrast, and noise, the quality of the images is crucial for accurate detection. The document specifies that shadow regions must be at least 5 pixels in size for effective analysis, and rocks must have a minimum diameter of 3 pixels (approximately 1.5 meters) to be detected, with a fully resolvable diameter of 5 pixels (2.5 meters) at the Lunar Reconnaissance Orbiter (LRO) NAC GSD of 0.5 meters.
Additionally, the document highlights the challenges posed by pattern noise in NAC EDR images, recommending the use of calibrated and de-noised images for better results. The research also draws comparisons to similar studies conducted on Mars, showcasing the broader implications of rock mapping technologies across different celestial bodies.
The document concludes with references to various studies and publications related to lunar and Martian geology, emphasizing the collaborative nature of this research and its potential applications in future space missions. Overall, this technical support package provides a comprehensive overview of the methodologies, challenges, and advancements in the field of automatic lunar rock detection and mapping, contributing to our understanding of the Moon's geological features and aiding in the planning of future exploration missions.
