Lukas Mandrake

Hemispheric Daytime Ionospheric Response to Intense Solar Wind Forcing

We investigate the ionospheric response to events where the z-component of the interplanetary magnetic field, B 2 , becomes large and negative for several hours, associated with the largest geomagnetic storms over the prior solar maximum period (2000-2004). We compute the average vertical total electron content (TEC) in the broad region covering 1200-1600 local time and ±40 degrees geomagnetic latitude (dipole), using data from the global network of Global Positioning System (GPS) receivers. In several cases, we find approximately a two-fold increase in total electron content within 2-3 hours of the time when the southward-B solar wind impinged on the magnetopause. We also analyze daytime super-satellite TEC data from the GPS receiver on the CHAMP satellite orbiting at approximately 400 km altitude, and find that for the October 30, 2003 storm at mid-latitudes the TEC increase is nearly one order of magnitude relative to the TEC just prior to the B southward onset. The geomagnetic storm-time phenomenon of prompt penetration electric fields into the ionosphere from enhanced magnetospheric convection is the most likely cause of these TEC increases, at least for certain of the events, resulting in eastward directed electric fields at the equator. The resulting dayside vertical ExB drift of plasma to higher altitudes, while solar photons create more plasma at lower altitudes, results in a daytime super-fountain effect that rapidly changes the plasma structure of the entire dayside ionosphere. This phenomenon has major practical space weather implications.

Improving onboard analysis of Hyperion images by filtering mislabeled training data examples

This paper presents PWEM, a technique for detecting class label noise in training data. PWEM detects mislabeled examples by assigning to each training example a probability that its label is correct. PWEM calculates this probability by clustering examples from pairs of classes together and analyzing the distribution of labels within each cluster to derive the probability of each labels correctness. We discuss how one can use the probabilities output by PWEM to filter, mitigate, or correct mislabeled training examples. We then provide an in-depth discussion of how we applied PWEM to a sulfur detector that labels pixels from Hyperion images of the Borup-Fiord pass in Northern Canada. PWEM assigned a large number of the sulfur training examples low probabilities, indicating severe mislabeling within the sulfur class. The filtering of those low confidence examples resulted in a cleaner training set and improved the median false positive rate of the classifier by at least 29%.

A Case Study of Spectral Signature Detection in Multimodal and Outlier-Contaminated Scenes

Mapping localized spectral features in complex scenes demands sensitive and robust detection algorithms. This letter investigates two aspects of large images that can harm matched filter (MF) detection performance. First, multimodal backgrounds may violate normality assumptions. Second, outlier features can trigger false detections due to large projections onto the target vector. We review two state-of-the-art methods designed to resolve these issues. The background clustering of Funk models multimodal backgrounds, and the mixture-tuned (MT) MF of Boardman and Kruse addresses outliers. We demonstrate that combining the two methods has additional performance benefits. An MT cluster MF shows effective performance on simulated and airborne data sets. We demonstrate target detection scenarios that evidence multimodality, outliers, and their combination. These experiments explore the performance of the component algorithms and the practical circumstances that can favor a combined approach.

Rapid Response to Volcanic Eruptions with an Autonomous Sensor Web: The Nyamulagira Eruption of 2006

Rapid response to alerts of impending or active volcanism is vital in the assessment of volcanic risk and hazard. The JPL model-driven volcano sensor Web (MSW) demonstrated such an autonomous response during a volcanic crisis at Nyamulagira volcano, D. R. Congo, in December 2006, quickly providing vital information to volcanologists in the field. The MSW was developed to enable fast science-driven asset command and control. Alerts of volcanic activity from around the world are used to trigger high resolution observations (both spectral and spatial) by the EO-1 spacecraft. Data are processed onboard EO-1 by advanced software (the autonomous sciencecraft experiment [ASE]). If volcanic thermal emission is detected, ASE retasks EO-1 to obtain more data. A summary of the observation is returned within two hours of data acquisition.

Onboard SVM analysis of Hyperion data to detect sulfur deposits in Arctic regions

Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.

Onboard detection of natural sulfur on a glacier via a SVM and Hyperion data

Onboard classification of remote sensing data is of general interest given that it can be used as a trigger to initiate alarms, data download, additional higher-resolution scans, or more frequent scans of an area without ground interaction. In our case, we study the sulfur-rich Borup-Fiord glacial springs in Canada utilizing the Hyperion instrument aboard the EO-1 spacecraft. This system consists of naturally occurring sulfur-rich springs emerging from glacial ice, which are a known environment for microbial life. The biological activity of the spring is associated with sulfur compounds that can be detected remotely via spectral analysis. This system may offer an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Unfortunately, spacecraft processing power and memory is severely limited which places strong constraints on the algorithms available. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of sulfur compounds associated with the activity of microbial life. However, those results were limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. Of key interest is our assessment of the classifiers behavior on non-sulfur-bearing imagery far from the training region. Selection of the most relevant spectral bands and parameters for the SVM are also explored.

Quantification of trace chemicals using Vehicle Cabin Atmosphere Monitor

A system to monitor the concentrations of trace chemicals in cabin atmosphere is one of the most critical components in long-duration human flight missions. The Vehicle Cabin Atmosphere Monitor (VCAM) is a miniature gas chromatograph mass spectrometer system to be used to detect and quantify trace chemicals in the International Space Station. We developed an autonomous computational process to quantify trace chemicals for use in VCAM. The process involves the design of a measured signal quantification scheme, the construction of concentration curves (i.e. the relationship between concentration and ion count measured by VCAM), the decision rule of applying high- or low-gain concentration curves, and the detection of saturation, low-signals, and outliers. When the developed quantification process is applied, the average errors of concentration for most of trace chemicals are found to be between 14% and 66%.

Automated Neutral Region selection using superpixels

This work presents an automated approach utilizing superpixel segmentation for detecting spectrally Neutral Regions (NR) in hyperspectral images. NRs are often used in planetary geology as spectral divisors to Regions of Interest (ROI), both to enhance key mineralogical signatures and correct for systematic errors such as residual atmospheric distortion. We compare automated NR selections to handpicked examples with mineralogical summary products used in analysis of data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM). We also present a new summary product to quantify the level of atmospheric distortion in a CRISM spectrum. We find that the automated algorithm matches manual NR detection with regards to mineral spectral contrast and outperforms manual selection for reducing atmospheric distortion.

Adapting AMDIS for autonomous spectral identification of hazardous compounds for ISS monitoring

The stand-alone Vehicle Cabin Atmospheric Monitor (VCAM) instrument was designed to provide an automated method of monitoring air quality within the International Space Station (ISS) via a miniaturized mass spectrometer and gas chromatograph system. The output of the device, a series of mass spectra as a function of time, is then processed via our implementation of the Automated Mass Spectral Deconvolution and Identification System (AMDIS) method from the National Institute for Standards and Technology (NIST) to generate potential identification with reference to a known library of hazardous chemicals. In this paper we discuss the modifications required to the AMDIS method for autonomous in-flight operation as well as additions beyond the original method. In particular, the original AMDIS method contains numerous parameters that were intended to be adjusted by an operator during the analysis to reduce false positives and adjust sensitivity. We have instead implemented solution filtration based on elution time and discuss possible arbitration algorithms for close similar matches to provide the user with a more succinct, single-valued answer.

Surface Sulfur Detection via Remote Sensing and Onboard Classification

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.