Next-generation orbital imaging spectrometers will generate unprecedented data volumes, demanding new methods to optimize storage and communication resources. Here, we demonstrate that onboard analysis can excise cloud-contaminated scenes, reducing data volumes while preserving science return. We calculate optimal cloud-screening parameters in advance, exploiting stable radiometric calibration and foreknowledge of illumination and viewing geometry. Channel thresholds expressed in raw instrument values can be then uploaded to the sensor where they execute in real time at gigabit-per-second (Gb/s) data rates. We present a decision theoretic method for setting these instrument parameters and characterize performance using a continuous three-year image archive from the “classic” Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-C). We then simulate the system onboard the International Space Station, where it provides factor-of-two improvements in data volume with negligible false positives. Finally, we describe a real-time demonstration onboard the AVIRIS Next Generation (AVIRIS-NG) flight platform during a recent science campaign. In this blind test, cloud screening is performed without error while keeping pace with instrument data rates.

Imaging spectrometers are valuable instruments for space exploration, but their large data volumes limit the number of scenes that can be downlinked. Missions could improve science yield by acquiring surplus images and analyzing them onboard the spacecraft. This onboard analysis could generate surficial maps, summarizing scenes in a bandwidth-efficient manner to indicate data cubes that warrant a complete downlink. Additionally, onboard analysis could detect targets of opportunity and trigger immediate automated follow-up measurements by the spacecraft. Here, we report a first step toward these goals with demonstrations of fully automatic hyperspectral scene analysis, feature discovery, and mapping onboard the Earth Observing One (EO-1) spacecraft. We describe a series of overflights in which the spacecraft analyzes a scene and produces summary maps along with lists of salient features for prioritized downlink. The onboard system uses a superpixel endmember detection approach to identify compositionally distinctive features in each image. This procedure suits the limited computing resources of the EO-1 flight processor. It requires very little advance information about the anticipated spectral features, but the resulting surface composition maps agree well with canonical human interpretations. Identical spacecraft commands detect outlier spectral features in multiple scenarios having different constituents and imaging conditions.

The Autonomous Exploration for Gathering Increased Science (AEGIS) system enables automated data collection by planetary rovers. AEGIS software was uploaded to the Mars Exploration Rover (MER) mission’s Opportunity rover in December 2009 and has successfully demonstrated automated onboard targeting based on scientist-specified objectives. Prior to AEGIS, images were transmitted from the rover to the operations team on Earth; scientists manually analyzed the images, selected geological targets for the rover’s remote-sensing instruments, and then generated a command sequence to execute the new measurements. AEGIS represents a significant paradigm shift---by using onboard data analysis techniques, the AEGIS software uses scientist input to select high-quality science targets with no human in the loop. This approach allows the rover to autonomously select and sequence targeted observations in an opportunistic fashion, which is particularly applicable for narrow field-of-view instruments (such as the MER Mini-TES spectrometer, the MER Panoramic camera, and the 2011 Mars Science Laboratory (MSL) ChemCam spectrometer). This article provides an overview of the AEGIS automated targeting capability and describes how it is currently being used onboard the MER mission Opportunity rover.

Current and proposed remote space missions, such as the proposed aerial exploration of Titan by an aerobot, often can collect more data than can be communicated back to Earth. Autonomous selective downlink algorithms can choose informative subsets of data to improve the science value of these bandwidth-limited transmissions. This requires statistical descriptors of the data that reflect very abstract and subtle distinctions in science content. We propose a metric learning strategy that teaches algorithms how best to cluster new data based on training examples supplied by domain scientists. We demonstrate that clustering informed by metric learning produces results that more closely match multiple scientists’ labelings of aerial data than do clusterings based on random or periodic sampling. A new metric-learning strategy accommodates training sets produced by multiple scientists with different and potentially inconsistent mission objectives. Our methods are fit for current spacecraft processors (e.g., RAD750) and would further benefit from more advanced spacecraft processor architectures, such as OPERA.

Orbital remote sensing provides a powerful way to efficiently survey targets such as the Earth and other planets and moons for features of interest. One such feature of astrobiological relevance is the presence of surface sulfur deposits. These deposits have been observed to be associated with microbial activity at the Borup Fiord glacial springs in Canada, a location that may provide an analogue to other icy environments such as Europa. This article evaluates automated classifiers for detecting sulfur in remote sensing observations by the hyperion spectrometer on the EO-1 spacecraft. We determined that a data-driven machine learning solution was needed because the sulfur could not be detected by simply matching observations to sulfur lab spectra. We also evaluated several methods (manual and automated) for identifying the most relevant attributes (spectral wavelengths) needed for successful sulfur detection. Our findings include (1) the Borup Fiord sulfur deposits were best modeled as containing two sub-populations: sulfur on ice and sulfur on rock; (2) as expected, classifiers using Gaussian kernels outperformed those based on linear kernels, and should be adopted when onboard computational constraints permit; and (3) Recursive Feature Elimination selected sensible and effective features for use in the computationally constrained environment onboard EO-1. This study helped guide the selection of algorithm parameters and configuration for the classification system currently operational on EO-1. Finally, we discuss implications for a similar onboard classification system for a future Europa orbiter.

This paper describes the radiometric calibration of the original Orbiting Carbon Observatory. The calibration process required characterizing both the dark current level and gain coefficients of each instrumental channel. The dark response was characterized with extensive testing and revealed some unexpected instrument behavior. The gain coefficients were characterized via illumination of the instrument spectrometers with a laboratory calibrated integrating sphere source. Comparison between the spectrometer output and a calibrated photodiode led to a set of calibration coefficients for each spectrometer channel. The calibration coefficients were validated by a novel approach involving observation of the solar spectrum through a transmission filter. Validation occurred by examination of both the ratio of the filtered to unfiltered spectra and retrievals of geophysical quantities such as surface pressure. The linearity of the calibration was established to approximately 0.2%. Finally, using the calibration data from the integrating sphere, a simple noise model was developed for each channel of the instrument. A summary of the signal to-noise performance is included.

Superpixels are homogeneous image regions comprised of multiple contiguous pixels. Superpixel representations can reduce noise in hyperspectral images by exploiting the spatial contiguity of scene features. This paper combines superpixels with endmember extraction to produce concise mineralogical summaries that assist in browsing large image catalogs. First, a graph-based agglomerative algorithm oversegments the image. We then use segments' mean spectra as input to existing statistical endmember detection algorithms such as sequential maximum angle convex cone (SMACC) and N-FINDR. Experiments compare automatically detected endmembers to target minerals in an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scene of Cuprite, Nevada. We also consider a planetary science data set from the Compact Reconnaissance Imaging Spectrometer (CRISM) instrument that benefits from spatial averaging due to higher noise. In both cases, superpixel representations significantly reduce the computational complexity of later processing while improving endmembers' match to the target spectra.

This paper presents technology for performing autonomous commanding of a planetary rover. Through the use of AI planning, scheduling and execution techniques, the OASIS autonomous science system provides capabilities for the automated generation of a rover activity plan based on science priorities, the handling of opportunistic science, including new science targets identified by onboard data analysis software, other dynamic decision-making such as modifying the rover activity plan in response to problems or other state and resource changes. We first describe some of the particular challenges this work has begun to address and then describe our system approach. Finally, we report on our experience testing this software with a Mars rover prototype.

Analyzing data on-board a spacecraft as it is collected enables several advanced spacecraft capabilities, such as prioritizing observations to make the best use of limited bandwidth and reacting to dynamic events as they happen. In this paper, we describe how we addressed the unique challenges associated with on-board mining of data as it is collected: uncalibrated data, noisy observations, and severe limitations on computational and memory resources. The goal of this effort, which falls into the emerging application area of spacecraft-based data mining, was to study three specific science phenomena on Mars. Following previous work that used a linear support vector machine (SVM) on-board the Earth Observing 1 (EO-1)spacecraft, we developed three data mining techniques for use on-board the Mars Odyssey spacecraft. These methods range from simple thresholding to state-of-the-art reduced-set SVM technology. We tested these algorithms on archived data in a flight software testbed. We also describe a significant, serendipitous science discovery of this data mining effort: the confirmation of a water ice annulus around the north polar cap of Mars. We conclude with a discussion on lessons learned in developing algorithms for use on-board a spacecraft.

Acquisition of science in space applications is shifting from teleoperated gathering to an automated on-board analysis with improvements in the use of memory, CPU, bandwidth and data quality. In this paper, we describe algorithms to autonomously detect dust devils and clouds from a rover and summarize the results. The algorithms meet high hit-to-miss ratios and satisfy strict resource constraints. Both detectors have been uploaded to the Mars exploration rovers (MER). These are the first autonomous science processes in the rovers.

Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a spacecraft to identify high priority data for downlink to Earth, providing a method for maximizing the use of a potentially bandwidth limited downlink channel. Onboard analysis can also enable rapid reaction to dynamic events, such as flooding, volcanic eruptions or sea ice break-up. Four classifiers were developed to identify cryosphere events using hyperspectral images. These classifiers include a manually constructed classifier, a Support Vector Machine (SVM), a Decision Tree and a classifier derived by searching over combinations of thresholded band ratios. Each of the classifiers was designed to run in the computationally constrained operating environment of the spacecraft. A set of scenes was hand-labeled to provide training and testing data. Performance results on the test data indicate that the SVM and manual classifiers outperformed the Decision Tree and band-ratio classifiers with the SVM yielding slightly better classifications than the manual classifier. The manual and SVM classifiers have been uploaded to the EO-1 spacecraft and have been running onboard the spacecraft for over a year. Results of the onboard analysis are used by the Autonomous Sciencecraft Experiment (ASE) of NASA’s New Millennium Program onboard EO-1 to automatically target the spacecraft to collect follow-on imagery. The software demonstrates the potential for future deep space missions to use onboard decision making to capture short-lived science events.

We describe a network of sensors linked by software and the Internet to an autonomous satellite observation response capability. This system of systems is designed with a flexible, modular, architecture to facilitate expansion in sensors, customization of trigger conditions, and customization of responses. This system has been used to implement a global surveillance program of science phenomena including: volcanoes, flooding, cryosphere events, and atmospheric phenomena. In this paper we describe the importance of the earth observing sensorweb application as well as overall architecture for the system of systems.

An Autonomous Science Agent has been flying onboard the Earth Observing One spacecraft since 2003. This software enables the spacecraft to autonomously detect and responds to science events occurring on the Earth such as volcanoes, flooding, and snow melt. This agent includes artificial intelligence software systems that perform science data analysis, deliberative planning, and run-time robust execution. This software is in routine use to fly the EO-1 mission. In this paper we discuss the architecture used to integrate these systems and lessons learned from its multi-year flight on EO-1.

We discuss techniques to predict the dynamic vehicle response to various natural obstacles. This method can then be used to adjust the vehicle dynamics to optimize performance (e.g. speed) while ensuring that the vehicle is not damaged. This capability opens up a new area of obstacle negotiation for UGVs, where the vehicle moves over certain obstacles, rather than avoiding them, thereby resulting in more effective achievement of objectives. Robust obstacle negotiation and vehicle dynamics prediction requires several key technologies that are discussed in this paper. We detect and segment (label) obstacles using a novel 3D obstacle algorithm. The material of each labelled obstacle (rock, vegetation, etc) is then determined using a texture or color classification scheme. Terrain load-bearing surface models are then constructed using vertical springs to model the compressibility and traversability of each obstacle in front of the vehicle. The terrain model is then combined with the vehicle suspension model to yield an estimate of the maximum safe velocity, and predict the vehicle dynamics as the vehicle follows a path. This end-to-end obstacle negotiation system is envisioned to be useful in optimized path planning and vehicle navigation in terrain conditions cluttered with vegetation, bushes, rocks, etc. Results on natural terrain with various natural materials are presented.