A software system has been developed for prioritizing newly acquired geological data onboard a planetary rover. The system has been designed to enable efficient use of limited communication resources by transmitting the data likely to have the most scientific value. This software operates onboard a rover by analyzing collected data, identifying potential scientific targets, and then using that information to prioritize data for transmission to Earth. Currently, the system is focused on the analysis of acquired images, although the general techniques are applicable to a wide range of data modalities. Image prioritization is performed using two main steps. In the first step, the software detects features of interest from each image. In its current application, the system is focused on visual properties of rocks. Thus, rocks are located in each image and rock properties, such as shape, texture, and albedo, are extracted from the identified rocks. In the second step, the features extracted from a group of images are used to prioritize the images using three different methods: (1) identification of key target signature (finding specific rock features the scientist has identified as important), (2) novelty detection (finding rocks we haven't seen before), and (3) representative rock sampling (finding the most average sample of each rock type). These methods use techniques such as K-means unsupervised clustering and a discrimination-based kernel classifier to rank images based on their interest level.
This program was written by Rebecca Castano, Robert Anderson, Tara Estlin, Dennis DeCoste, Daniel Gaines, Dominic Mazzoni, Forest Fisher, and Michele Judd of Caltech for NASA's Jet Propulsion Laboratory.
This software is available for commercial licensing. Please contact Don Hart of the California Institute of Technology at (818) 393-3425. Refer to NPO-40265.
This Brief includes a Technical Support Package (TSP).
Prioritizing Scientific Data for Transmission
(reference NPO-40265) is currently available for download from the TSP library.
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Overview
The document discusses the challenges of data prioritization in future planetary exploration missions, where the volume of data collected by scientific instruments often exceeds the transmission capacity of spacecraft. As the number of missions and the capabilities of instruments increase, the need for effective data management becomes critical. The Deep Space Network (DSN) faces constraints in bandwidth and power, necessitating innovative solutions to maximize scientific return.
To address these challenges, the document presents a suite of techniques for autonomously prioritizing science data onboard spacecraft before transmission. These methods focus on identifying and selecting data that represent the local geology and any unusual observations, ensuring that the most scientifically valuable information is transmitted back to Earth. The prioritization process is essential for making informed decisions about which data to downlink, as traditional data compression methods have limitations and can lead to significant distortion.
The authors emphasize the importance of developing onboard analysis capabilities that can evaluate the science value of data in real-time. This involves encapsulating data characteristics in a form that can be analyzed and ranked based on their scientific significance. The techniques described can be applied to various data types, including grayscale images, hyperspectral images, and both orbital and ground data applications.
One key objective highlighted is the need to ensure that the data downlinked includes representative samples of the region being studied, rather than focusing solely on interesting or unusual findings. This approach helps avoid bias in the data set, ensuring that less common rock types and features are also represented.
The document concludes that by implementing these autonomous prioritization techniques, missions can achieve their primary scientific exploration objectives more effectively. The proposed system allows for the extraction of properties from image data and the ranking of this data based on its measured science value, ultimately enhancing the quality of data returned to scientists.
In summary, the document outlines a strategic approach to managing the increasing data demands of planetary exploration missions through onboard data prioritization, ensuring that the most valuable scientific information is transmitted efficiently back to Earth.
