Regional surveys of CH4 point sources across North America: campaigns, algorithms, and latest results
No abstract provided
Dennison, Philip↗
Engineering topics
Bue, Brian
Publications and source records attributed to Bue, Brian.
Flood Mapping Using UAVSAR and Convolutional Neural Networks
We have mapped flooded areas in data collected by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) using two convolutional neural network (CNN) image classifier architectures: U-Net and SegNet. Our study area was a region around Houston, TX, USA affected by widespread flooding in 2017 due to Hurricane Harvey. To train and test the classifiers, we manually labelled over 10000 image segments in two flight lines. Both U-Net and SegNet yielded higher accuracy than a previous non-machine learning classifier we used as a baseline. U-Net had slightly higher accuracy than SegNet. The classifiers performed better in areas with more homogeneous land cover. To independently validate the classifier accuracy we used NOAA aerial imagery, with overall accuracy around 80%. Future work includes assessing the classifier robustness in other study areas, assessing the classifier dependence on UAVSAR incidence angle, particularly for open water and bare ground, and collecting more training data, particularly in urban areas. This study demonstrates the potential of CNN image classifiers for mapping flooded areas in airborne polarimetric SAR imagery, and for land cover classification of polarimetric SAR imagery more generally.
Denbina, Michael W↗
Modeling Key Predictors of Airport Runway Configurations Using Learning Algorithms
Advanced traffic flow management automation will need accurate predictions of airport runway configurations. Terminal area weather and traffic demand are generally considered to be the most significant factors in predicting runway configuration. Weather information is forecasted across multiple features, including wind direction, wind speed, gusts, cloud ceilings, visibility, temperature, and precipitation, among many others. We use machine learning techniques on historical weather and runway data to determine weather features that correlate well with runway configurations. We analyze the predictive capability of weather features using different learning models trained on data from four major U.S. airports: Atlanta (ATL), Washington – Dulles (IAD), New York – Kennedy (JFK), and San Francisco (SFO). Wind direction alone is strongly correlated with runway configurations above all other examined factors, as expected. This correlation is the most significant component of the ~80% prediction accuracy in selecting between the two most frequently used runway configurations. However, individual airports show variations on how well the runway configuration decisions correlate with wind direction. While wind direction was identified as the most significant indicator of configuration decisions in ATL, IAD, and JFK, it did not emerge as such at SFO. Traffic demand was not found to be a strong factor in predicting runway configurations at any of the airports analyzed. In rare instances, when high demand cannot be accommodated within the current configuration, temporary changes are likely to be attributable to demand. However, these occurrences are so limited in number that their overall effect is not sufficient to consider traffic demand as a major indicator of runway configuration at the airports analyzed.
Bilimoria, Karl D.↗
Agile Big Data Analytics of High-Volume Geodetic Data Products for Improving Science and Hazard Response
No abstract available
Hua, Hook↗
Metric Learning to Enhance Hyperspectral Image Segmentation
Unsupervised hyperspectral image segmentation can reveal spatial trends that show the physical structure of the scene to an analyst. They highlight borders and reveal areas of homogeneity and change. Segmentations are independently helpful for object recognition, and assist with automated production of symbolic maps. Additionally, a good segmentation can dramatically reduce the number of effective spectra in an image, enabling analyses that would otherwise be computationally prohibitive. Specifically, using an over-segmentation of the image instead of individual pixels can reduce noise and potentially improve the results of statistical post-analysis. In this innovation, a metric learning approach is presented to improve the performance of unsupervised hyperspectral image segmentation. The prototype demonstrations attempt a superpixel segmentation in which the image is conservatively over-segmented; that is, the single surface features may be split into multiple segments, but each individual segment, or superpixel, is ensured to have homogenous mineralogy.
Thompson, David R.↗
Hyperspectral Feature Detection Onboard the Earth Observing One Spacecraft
No abstract available
hyperspectral imagery↗
Efficient Spectral Endmember Detection Onboard the EO-1 Spacecraft
Spaceflight and planetary exploration place severe constraints on the available bandwidth for downlinking large hyperspectral images. In addition, communications with spacecraft often occur intermittently, so mission-relevant hyperspectral data must wait for analysis on the ground before it can inform spacecraft activity planning. Onboard endmember detection can help alleviate these problems. It enables novelty detection and target identification for scheduling follow-up activities such as additional observation by narrow field of view instruments. Additionally, endmember analysis can facilitate data summary for downlink. This work describes a planned experiment of selective downlink by the EO-1 autonomous spacecraft. Here an efficient superpixel endmember detection algorithm keeps to the limited computational constraints of the flight processor. Tests suggest the procedure could enable significant improvements in downlink efficiency.
Endmember Detection↗
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%.
Lee, Seungwon↗
Onboard Detection of Active Canadian Sulfur Springs: A Europa Analogue
We discuss a current, ongoing demonstration of insitu onboard detection in which the Earth Observing-1 spacecraft detects surface sulfur deposits that originate from underlying springs by distinguishing the sulfur from the ice-rich glacial background, a good analogue for the Europan surface. In this paper, we describe the process of developing the onboard classifier for detecting the presence of sulfur in a hyperspectral scene, including the use of a training/testing set that is not exhaustively labeled, i.e.not all true positives are marked, and the selection of 12, out of 242, Hyperion instrument wavelength bands to use in the onboard detector. This study aims to demonstrate the potential for future missions to capture short-lived science events, make decisions onboard, identify high priority data for downlink and perform onboard change detection. In the future, such capability could help maximize the science return of downlink bandwidth-limited missions, addressing a significant constraint in all deep-space missions.
autonomy↗