Publications by Colleges and Departments (MSU - Bozeman)
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Item Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data(MDPI AG, 2023-12) Vannoy, Trevor C.; Sweeney, Nathaniel B.; Shaw, Joseph A.; Whitaker, Bradley M.Concerns about decreases in insect population and biodiversity, in addition to the need for monitoring insects in agriculture and disease control, have led to an increased need for automated, non-invasive monitoring techniques. To this end, entomological lidar systems have been developed and successfully used for detecting and classifying insects. However, the data produced by these lidar systems create several problems from a data analysis standpoint: the data can contain millions of observations, very few observations contain insects, and the background environment is non-stationary. This study compares the insect-detection performance of various supervised machine learning and unsupervised changepoint detection algorithms and provides commentary on the relative strengths of each method. We found that the supervised methods generally perform better than the changepoint detection methods, at the cost of needing labeled data. The supervised learning method with the highest Matthew’s Correlation Coefficient score on the testing set correctly identified 99.5% of the insect-containing images and 83.7% of the non-insect images; similarly, the best changepoint detection method correctly identified 83.2% of the insect-containing images and 84.2% of the non-insect images. Our results show that both types of methods can reduce the need for manual data analysis.Item Agile collaboration: Citizen science as a transdisciplinary approach to heliophysics(Frontiers Media SA, 2023-04) Ledvina, Vincent; Brandt, Laura; MacDonald, Elizabeth; Frissell, Nathaniel; Anderson, Justin; Chen, Thomas Y.; French, Ryan J.; Mare, Francesca Di; Grover, Andrea; Sigsbee, Kristine; Gallardo-Lacourt, Bea; Lach, Donna; Shaw, Joseph A.; Hunnekuhl, Michael; Kosar, Burcu; Barkhouse, Wayne; Young, Tim; Kedhambadi, Chandresh; Ozturk, Dogacan S.; Claudepierre, Seth G.; Dong, Chuanfei; Witteman, Andy; Kuzub, Jeremy; Sinha, GunjanCitizen science connects scientists with the public to enable discovery, engaging broad audiences across the world. There are many attributes that make citizen science an asset to the field of heliophysics, including agile collaboration. Agility is the extent to which a person, group of people, technology, or project can work efficiently, pivot, and adapt to adversity. Citizen scientists are agile; they are adaptable and responsive. Citizen science projects and their underlying technology platforms are also agile in the software development sense, by utilizing beta testing and short timeframes to pivot in response to community needs. As they capture scientifically valuable data, citizen scientists can bring expertise from other fields to scientific teams. The impact of citizen science projects and communities means citizen scientists are a bridge between scientists and the public, facilitating the exchange of information. These attributes of citizen scientists form the framework of agile collaboration. In this paper, we contextualize agile collaboration primarily for aurora chasers, a group of citizen scientists actively engaged in projects and independent data gathering. Nevertheless, these insights scale across other domains and projects. Citizen science is an emerging yet proven way of enhancing the current research landscape. To tackle the next-generation’s biggest research problems, agile collaboration with citizen scientists will become necessary.Item Optical transmittance of 3D printing materials(Optica Publishing Group, 2021-07) Hamp, Shannon M.; Logan, Riley D.; Shaw, Joseph A.The increasing prevalence of three-dimensional (3D) printing of optical housings and mounts necessitates a better understanding of the optical properties of printing materials. This paper describes a method for using multithickness samples of 3D printing materials to measure transmittance spectra at wavelengths from 400 to 2400 nm [visible to short-wave infrared (IR)]. In this method, 3D samples with material thicknesses of 1, 2, 3, and 4 mm were positioned in front of a uniform light source with a spectrometer probe on the opposing side to measure the light transmittance. Transmission depended primarily on the thickness and color of the sample, and multiple scattering prevented the use of a simple exponential model to relate transmittance, extinction, and thickness. A Solidworks file and a 3D printer file are included with the paper to enable measurements of additional materials with the same method.Item Generalized Nighttime Radiative Deficits(Elsevier BV, 2021-10) Howell, John C.; Yizhaq, Tomer; Drechsler, Nadav; Zamir, Yuval; Beysens, Daniel; Shaw, Joseph A.We derive a general, tilt-dependent, nighttime, radiative deficit model with an eye towards improved dew collection. The model incorporates atmospheric/environmental incoming radiation, a linear precipitable water vapor transmittance function dependent on local meteo data and the influence of near-horizon obstacles. A brief discussion of cloud cover is given. The model is then used more specifically to predict radiative deficits for an ideal blackbody emitter in an environment with an isotropic temperature. Knowing the tilt angle, near-horizon obstacles and local meteo-data, it is then possible to estimate the radiative deficit of a given emitter. We consider errors resulting from the assumption that the ground and obstacles are at the same temperature as the air. We also analyze the errors arising from the linear precipitable water vapor transmittance function by comparing the results against high-resolution, full-spectrum Modtran® data [1]. We show that for typical tilt angles, the isotropic temperature model is a reasonable approximation as long as the above-horizon environmental heating is small. We believe these results will be broadly valuable for the field of radiative cooling where a general radiative treatment has yet to be made and in particular the field of dew water harvesting.Item Trutinor: A Conceptual Study for a Next-Generation Earth Radiant Energy Instrument(MDPI, 2020-10) Young, Cindy L.; Lukashin, Constantine; Taylor, Patrick C.; Swanson, Rand; Kirk, William S.; Cooney, Michael; Swartz, William H.; Goldberg, Arnold; Stone, Thomas; Jackson, Trevor; Doelling, David R.; Shaw, Joseph A.; Buleri, ChristineUninterrupted and overlapping satellite instrument measurements of Earth’s radiation budget from space are required to sufficiently monitor the planet’s changing climate, detect trends in key climate variables, constrain climate models, and quantify climate feedbacks. The Clouds and Earth’s Radiant Energy System (CERES) instruments are currently making these vital measurements for the scientific community and society, but with modern technologies, there are more efficient and cost-effective alternatives to the CERES implementation. We present a compact radiometer concept, Trutinor (meaning “balance” in Latin), with two broadband channels, shortwave (0.2–3 μm) and longwave (5–50 μm), capable of continuing the CERES record by flying in formation with an existing imager on another satellite platform. The instrument uses a three-mirror off-axis anastigmat telescope as the front optics to image these broadband radiances onto a microbolometer array coated with gold black, providing the required performance across the full spectral range. Each pixel of the sensor has a field of view of 0.6°, which was chosen so the shortwave band can be efficiently calibrated using the Moon as an on-orbit light source with the same angular extent, thereby reducing mass and improving measurement accuracy, towards the goal of a gap-tolerant observing system. The longwave band will utilize compact blackbodies with phase-change cells for an absolute calibration reference, establishing a clear path for SI-traceability. Trutinor’s instrument breadboard has been designed and is currently being built and tested.Item Reduced-cost hyperspectral convolutional neural networks(2020-09) Morales, Giorgio; Sheppard, John W.; Scherrer, Bryan; Shaw, Joseph A.Hyperspectral imaging provides a useful tool for extracting complex information when visual spectral bands are not enough to solve certain tasks. However, processing hyperspectral images (HSIs) is usually computationally expensive due to the great amount of both spatial and spectral data they incorporate. We present a low-cost convolutional neural network designed for HSI classification. Its architecture consists of two parts: a series of densely connected three-dimensional (3-D) convolutions used as a feature extractor, and a series of two-dimensional (2-D) separable convolutions used as a spatial encoder. We show that this design involves fewer trainable parameters compared to other approaches, yet without detriment to its performance. What is more, we achieve comparable state-of-the-art results testing our architecture on four public remote sensing datasets: Indian Pines, Pavia University, Salinas, and EuroSAT; and a dataset of Kochia leaves [Bassia scoparia] with three different levels of herbicide resistance. The source code and datasets are available online.Item Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks(2021) Morales, Giorgio; Sheppard, John W.; Logan, Riley D.; Shaw, Joseph A.In recent years, Hyperspectral Imaging (HSI) has become a powerful source for reliable data in applications such as remote sensing, agriculture, and biomedicine. However, hyperspectral images are highly data-dense and often benefit from methods to reduce the number of spectral bands while retaining the most useful information for a specific application. We propose a novel band selection method to select a reduced set of wavelengths, obtained from an HSI system in the context of image classification. Our approach consists of two main steps: the first utilizes a filter-based approach to find relevant spectral bands based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. The second step applies a wrapper-based approach to select bands from the reduced set based on their information entropy values, and trains a compact Convolutional Neural Network (CNN) to evaluate the performance of the current selection. We present classification results obtained from our method and compare them to other feature selection methods on two hyperspectral image datasets. Additionally, we use the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager. We show that our method produces more suitable results for a multispectral sensor design.Item Hyperspectral Dimensionality Reduction Based on Inter-Band Redundancy Analysis and Greedy Spectral Selection(2021-09) Morales, Giorgio; Sheppard, John W.; Logan, Riley D.; Shaw, Joseph A.Hyperspectral imaging systems are becoming widely used due to their increasing accessibility and their ability to provide detailed spectral responses based on hundreds of spectral bands. However, the resulting hyperspectral images (HSIs) come at the cost of increased storage requirements, increased computational time to process, and highly redundant data. Thus, dimensionality reduction techniques are necessary to decrease the number of spectral bands while retaining the most useful information. Our contribution is two-fold: First, we propose a filter-based method called interband redundancy analysis (IBRA) based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. Second, we apply a wrapper-based approach called greedy spectral selection (GSS) to the results of IBRA to select bands based on their information entropy values and train a compact convolutional neural network to evaluate the performance of the current selection. We also propose a feature extraction framework that consists of two main steps: first, it reduces the total number of bands using IBRA; then, it can use any feature extraction method to obtain the desired number of feature channels. We present classification results obtained from our methods and compare them to other dimensionality reduction methods on three hyperspectral image datasets. Additionally, we used the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager.Item Digital all-sky polarization imaging of the total solar eclipse on 21 August 2017 in Rexburg, Idaho, USA(2020-07) Eshelman, Laura M.; Tauc, Martin Jan; Hashimoto, Taiga; Gillis, Kendra; Weiss, William; Stanley, Bryan; Hooser, Preston; Shaw, Glenn E.; Shaw, Joseph A.All-sky polarization images were measured from sunrise to sunset and during a cloud-free totality on 21 August 2017 in Rexburg, Idaho using two digital three-camera all-sky polarimeters and a time-sequential liquid-crystal-based all-sky polarimeter. Twenty-five polarimetric images were recorded during totality, revealing a highly dynamic evolution of the distribution of skylight polarization, with the degree of linear polarization becoming nearly zenith-symmetric by the end of totality. The surrounding environment was characterized with an infrared cloud imager that confirmed the complete absence of clouds during totality, an AERONET solar radiometer that measured aerosol properties, a portable weather station, and a hand-held spectrometer with satellite images that measured surface reflectance at and near the observation site. These observations confirm that previously observed totality patterns are general and not unique to those specific eclipses. The high temporal image resolution revealed a transition of a neutral point from the zenith in totality to the normal Babinet point just above the Sun after third contact, providing the first indication that the transition between totality and normal daytime polarization patterns occurs over of a time period of approximately 13 s.Item Detection of polarization neutral points in observations of the combined corona and sky during the 21 August 2017 total solar eclipse(2020-07) Snik, Frans; Bos, Steven P.; Brackenhoff, Stefanie A.; Doelman, David S.; Por, Emiel H.; Bettonvil, Felix; Rodenhuis, Michiel; Vorobiev, Dmitry; Eshelman, Laura M.; Shaw, Joseph A.We report the results of polarimetric observations of the total solar eclipse of 21 August 2017 from Rexburg, Idaho (USA). We use three synchronized DSLR cameras with polarization filters oriented at 0°, 60°, and 120° to provide high-dynamic-range RGB polarization images of the corona and surrounding sky. We measure tangential coronal polarization and vertical sky polarization, both as expected. These observations provide detailed detections of polarization neutral points above and below the eclipsed Sun where the coronal polarization is canceled by the sky polarization. We name these special polarization neutral points after Minnaert and Van de Hulst.
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