Adaptive Similarity Measures for Material Identification in Hyperspectral Imagery

Remotely-sensed hyperspectral imagery has become one the most advanced tools for analyzing the processes that shape the Earth and other planets. Effective, rapid analysis of high-volume, high-dimensional hyperspectral image data sets demands efficient, automated techniques to identify signatures of known materials in such imagery. In this thesis, we develop a framework for automatic material identification in hyperspectral imagery using adaptive similarity measures. We frame the material identification problem as a multiclass similarity-based classification problem, where our goal is to predict material labels for unlabeled target spectra based upon their similarities to source spectra with known material labels. As differences in capture conditions affect the spectral representations of materials, we divide the material identification problem into intra-domain (i.e., source and target spectra captured under identical conditions) and inter-domain (i.e., source and target spectra captured under different conditions) settings. The first component of this thesis develops adaptive similarity measures for intra-domain settings that measure the relevance of spectral features to the given classification task using small amounts of labeled data. We propose a technique based on multiclass Linear Discriminant Analysis (LDA) that combines several distinct similarity measures into a single hybrid measure capturing the strengths of each of the individual measures. We also provide a comparative survey of techniques for low-rank Mahalanobis metric learning, and demonstrate that regularized LDA yields competitive results to the state-of-the-art, at substantially lower computational cost. The second component of this thesis shifts the focus to inter-domain settings, and proposes a multiclass domain adaptation framework that reconciles systematic differences between spectra captured under similar, but not identical, conditions. Our framework computes a similarity-based mapping that captures structured, relative relationships between classes shared between source and target domains, allowing us apply a classifier trained using labeled source spectra to classify target spectra. We demonstrate improved domain adaptation accuracy in comparison to recently-proposed multitask learning and manifold alignment techniques in several case studies involving state-of-the-art synthetic and real-world hyperspectral imagery

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

A Quantitative Validation of Multi-Modal Image Fusion and Segmentation for Object Detection and Tracking

In previous works, we have shown the efficacy of using Deep Belief Networks, paired with clustering, to identify distinct classes of objects within remotely sensed data via cluster analysis and qualitative analysis of the output data in comparison with reference data. In this paper, we quantitatively validate the methodology against datasets currently being generated and used within the remote sensing community, as well as show the capabilities and benefits of the data fusion methodologies used. The experiments run take the output of our unsupervised fusion and segmentation methodology and map them to various labeled datasets at different levels of global coverage and granularity in order to test our models’ capabilities to represent structure at finer and broader scales, using many different kinds of instrumentation, that can be fused when applicable. In all cases tested, our models show a strong ability to segment the objects within input scenes, use multiple datasets fused together where appropriate to improve results, and, at times, outperform the pre-existing datasets. The success here will allow this methodology to be used within use concrete cases and become the basis for future dynamic object tracking across datasets from various remote sensing instruments

Mini-Membrane Evaporator for Contingency Spacesuit Cooling

The next-generation Advanced Extravehicular Mobility Unit (AEMU) Portable Life Support System (PLSS) is integrating a number of new technologies to improve reliability and functionality. One of these improvements is the development of the Auxiliary Cooling Loop (ACL) for contingency crewmember cooling. The ACL is a completely redundant, independent cooling system that consists of a small evaporative cooler--the Mini Membrane Evaporator (Mini-ME), independent pump, independent feedwater assembly and independent Liquid Cooling Garment (LCG). The Mini-ME utilizes the same hollow fiber technology featured in the full-sized AEMU PLSS cooling device, the Spacesuit Water Membrane Evaporator (SWME), but Mini-ME occupies only approximately 25% of the volume of SWME, thereby providing only the necessary crewmember cooling in a contingency situation. The ACL provides a number of benefits when compared with the current EMU PLSS contingency cooling technology, which relies upon a Secondary Oxygen Vessel; contingency crewmember cooling can be provided for a longer period of time, more contingency situations can be accounted for, no reliance on a Secondary Oxygen Vessel (SOV) for contingency cooling--thereby allowing a reduction in SOV size and pressure, and the ACL can be recharged-allowing the AEMU PLSS to be reused, even after a contingency event. The first iteration of Mini-ME was developed and tested in-house. Mini-ME is currently packaged in AEMU PLSS 2.0, where it is being tested in environments and situations that are representative of potential future Extravehicular Activities (EVA's). The second iteration of Mini-ME, known as Mini-ME2, is currently being developed to offer more heat rejection capability. The development of this contingency evaporative cooling system will contribute to a more robust and comprehensive AEMU PLSS

Small Near-Earth Asteroids in the Palomar Transient Factory Survey: a Real-Time Streak-detection System

Near-Earth asteroids (NEAs) in the 1–100 meter size range are estimated to be ~1,000 times more numerous than the ~15,000 currently cataloged NEAs, most of which are in the 0.5–10 kilometer size range. Impacts from 10–100 meter size NEAs are not statistically life-threatening, but may cause significant regional damage, while 1–10 meter size NEAs with low velocities relative to Earth are compelling targets for space missions. We describe the implementation and initial results of a real-time NEA-discovery system specialized for the detection of small, high angular rate (visually streaked) NEAs in Palomar Transient Factory (PTF) images. PTF is a 1.2-m aperture, 7.3 deg^2 field of view (FOV) optical survey designed primarily for the discovery of extragalactic transients (e.g., supernovae) in 60-second exposures reaching ~20.5 visual magnitude. Our real-time NEA discovery pipeline uses a machine-learned classifier to filter a large number of false-positive streak detections, permitting a human scanner to efficiently and remotely identify real asteroid streaks during the night. Upon recognition of a streaked NEA detection (typically within an hour of the discovery exposure), the scanner triggers follow-up with the same telescope and posts the observations to the Minor Planet Center for worldwide confirmation. We describe our 11 initial confirmed discoveries, all small NEAs that passed 0.3–15 lunar distances from Earth. Lastly, we derive useful scaling laws for comparing streaked-NEA-detection capabilities of different surveys as a function of their hardware and survey-pattern characteristics. This work most directly informs estimates of the streak-detection capabilities of the Zwicky Transient Facility (ZTF, planned to succeed PTF in 2017), which will apply PTF's current resolution and sensitivity over a 47-deg^2 FOV

Processing Images from the Zwicky Transient Facility

The Zwicky Transient Facility is a new robotic-observing program, in which a newly engineered 600-MP digital camera with a pioneeringly large field of view, 47~square degrees, will be installed into the 48-inch Samuel Oschin Telescope at the Palomar Observatory. The camera will generate $\sim 1$~petabyte of raw image data over three years of operations. In parallel related work, new hardware and software systems are being developed to process these data in real time and build a long-term archive for the processed products. The first public release of archived products is planned for early 2019, which will include processed images and astronomical-source catalogs of the northern sky in the $g$ and $r$ bands. Source catalogs based on two different methods will be generated for the archive: aperture photometry and point-spread-function fitting.Comment: 6 pages, 4 figures, submitted to RTSRE Proceedings (www.rtsre.org

Airborne methane remote measurements reveal heavy-tail flux distribution in Four Corners region

Methane (CH_4) impacts climate as the second strongest anthropogenic greenhouse gas and air quality by influencing tropospheric ozone levels. Space-based observations have identified the Four Corners region in the Southwest United States as an area of large CH_4 enhancements. We conducted an airborne campaign in Four Corners during April 2015 with the next-generation Airborne Visible/Infrared Imaging Spectrometer (near-infrared) and Hyperspectral Thermal Emission Spectrometer (thermal infrared) imaging spectrometers to better understand the source of methane by measuring methane plumes at 1- to 3-m spatial resolution. Our analysis detected more than 250 individual methane plumes from fossil fuel harvesting, processing, and distributing infrastructures, spanning an emission range from the detection limit ∼2 kg/h to 5 kg/h through ∼5,000 kg/h. Observed sources include gas processing facilities, storage tanks, pipeline leaks, and well pads, as well as a coal mine venting shaft. Overall, plume enhancements and inferred fluxes follow a lognormal distribution, with the top 10% emitters contributing 49 to 66% to the inferred total point source flux of 0.23 Tg/y to 0.39 Tg/y. With the observed confirmation of a lognormal emission distribution, this airborne observing strategy and its ability to locate previously unknown point sources in real time provides an efficient and effective method to identify and mitigate major emissions contributors over a wide geographic area. With improved instrumentation, this capability scales to spaceborne applications [Thompson DR, et al. (2016) Geophys Res Lett 43(12):6571–6578]. Further illustration of this potential is demonstrated with two detected, confirmed, and repaired pipeline leaks during the campaign

SN2002es-like Supernovae from Different Viewing Angles

In this article, we compare optical light curves of two SN2002es-like Type Ia supernovae (SNe), iPTF14atg and iPTF14dpk, from the intermediate Palomar Transient Factory. Although the two light curves resemble each other around and after maximum, they show distinct early-phase rise behavior in the r-band. On the one hand, iPTF14atg revealed a slow and steady rise that lasted for 22 days with a mean rise rate of 0.2–0.3 mag day^(-1), before it reached the R-band peak (−18.05 mag). On the other hand, iPTF14dpk rose rapidly to −17 mag within a day of discovery with a rise rate > 1.8 mag day^(-1) , and then rose slowly to its peak (−18.19 mag) with a rise rate similar to iPTF14atg. The apparent total rise time of iPTF14dpk is therefore only 16 days. We show that emission from iPTF14atg before −17 days with respect to its maximum can be entirely attributed to radiation produced by collision between the SN and its companion star. Such emission is absent from iPTF14dpk probably because of an unfavored viewing angle, provided that SN2002es-like events arise from the same progenitor channel. We further show that an SN2002es-like SN may experience a dark phase after the explosion but before its radioactively powered light curve becomes visible. This dark phase may be lit by radiation from supernova–companion interaction