Hubble and Spitzer Space Telescope Observations of the Debris Disk around the Nearby K Dwarf HD 92945

[ABRIDGED] We present the first resolved images of the debris disk around the nearby K dwarf HD 92945. Our F606W (V) and F814W (I) HST/ACS coronagraphic images reveal an inclined, axisymmetric disk consisting of an inner ring 2".0-3".0 (43-65 AU) from the star and an extended outer disk whose surface brightness declines slowly with increasing radius 3".0-5".1 (65-110 AU) from the star. A precipitous drop in the surface brightness beyond 110 AU suggests that the outer disk is truncated at that distance. The radial surface-density profile is peaked at both the inner ring and the outer edge of the disk. The dust in the outer disk scatters neutrally but isotropically, and it has a low V-band albedo of 0.1. We also present new Spitzer MIPS photometry and IRS spectra of HD 92945. These data reveal no infrared excess from the disk shortward of 30 micron and constrain the width of the 70 micron source to < 180 AU. Assuming the dust comprises compact grains of astronomical silicate with a surface-density profile described by our scattered-light model of the disk, we successfully model the 24-350 micron emission with a minimum grain size of a_min = 4.5 micron and a size distribution proportional to a^-3.7 throughout the disk, but with a maximum grain size of 900 micron in the inner ring and 50 micron in the outer disk. Our observations indicate a total dust mass of ~0.001 M_earth. However, they provide contradictory evidence of the dust's physical characteristics: its neutral V-I color and lack of 24 micron emission imply grains larger than a few microns, but its isotropic scattering and low albedo suggest a large population of submicron-sized grains. The dynamical causes of the disk's morphology are unclear, but recent models of dust creation and transport in the presence of migrating planets indicate an advanced state of planet formation around HD 92945.Comment: 29 pages, 10 figures; to be published in The Astronomical Journa

Sedimentological characterization of Antarctic moraines using UAVs and Structure-from-Motion photogrammetry

In glacial environments particle-size analysis of moraines provides insights into clast origin, transport history, depositional mechanism and processes of reworking. Traditional methods for grain-size classification are labour-intensive, physically intrusive and are limited to patch-scale (1m2) observation. We develop emerging, high-resolution ground- and unmanned aerial vehicle-based ‘Structure-from-Motion’ (UAV-SfM) photogrammetry to recover grain-size information across an moraine surface in the Heritage Range, Antarctica. SfM data products were benchmarked against equivalent datasets acquired using terrestrial laser scanning, and were found to be accurate to within 1.7 and 50mm for patch- and site-scale modelling, respectively. Grain-size distributions were obtained through digital grain classification, or ‘photo-sieving’, of patch-scale SfM orthoimagery. Photo-sieved distributions were accurate to <2mm compared to control distributions derived from dry sieving. A relationship between patch-scale median grain size and the standard deviation of local surface elevations was applied to a site-scale UAV-SfM model to facilitate upscaling and the production of a spatially continuous map of the median grain size across a 0.3 km2 area of moraine. This highly automated workflow for site scale sedimentological characterization eliminates much of the subjectivity associated with traditional methods and forms a sound basis for subsequent glaciological process interpretation and analysis

Hard-Hearted Scrolls: A Noninvasive Method for Reading the Herculaneum Papyri

The Herculaneum scrolls were buried and carbonized by the eruption of Mount Vesuvius in A.D. 79 and represent the only classical library discovered in situ. Charred by the heat of the eruption, the scrolls are extremely fragile. Since their discovery two centuries ago, some scrolls have been physically opened, leading to some textual recovery but also widespread damage. Many other scrolls remain in rolled form, with unknown contents. More recently, various noninvasive methods have been attempted to reveal the hidden contents of these scrolls using advanced imaging. Unfortunately, their complex internal structure and lack of clear ink contrast has prevented these efforts from successfully revealing their contents. This work presents a machine learning-based method to reveal the hidden contents of the Herculaneum scrolls, trained using a novel geometric framework linking 3D X-ray CT images with 2D surface imagery of scroll fragments. The method is verified against known ground truth using scroll fragments with exposed text. Some results are also presented of hidden characters revealed using this method, the first to be revealed noninvasively from this collection. Extensions to the method, generalizing the machine learning component to other multimodal transformations, are presented. These are capable not only of revealing the hidden ink, but also of generating rendered images of scroll interiors as if they were photographed in color prior to their damage two thousand years ago. The application of these methods to other domains is discussed, and an additional chapter discusses the Vesuvius Challenge, a $1,000,000+ open research contest based on the dataset built as a part of this work

IR THERMOGRAPHY AND IMAGE PHOTOPLETHYSMOGRAPHY : NON-CONTACT MEDICAL COUNTER MEASURES FOR INFECTION SCREENING

Screening based on non-contact infrared thermometers (NCITs) and IRTGs shows promising results for mass fever screening. IRTGs were found to be powerful, quick and non-invasive methods to detect elevated temperatures. In the case of temperature measurement using IRTGs, regions medically adjacent to inner canthi are preferred sites for fever screening (IEC 80601-2-59:2008), which show good stability and correlation with body temperature. However, detection of canthi in thermal images is challenging due to the absence of features unlike visible images, which have sharp features that can be used for eye corner detection. We use registration of thermal images with visible light images (also called white-light images) to localize canthi regions in thermal images. We study the accuracy of such multi-modal image registration in the context of canthi detection and measure the feasibility of automatic canthi based temperature measurement as an alternative to manual measurement. The second part of thesis refers to the study of image photoplethysmography (IPPG), a cost-effective and flexible method for heart rate monitoring using videos recorded in ambient light. We use low-cost commercial grade video recording equipment (mobile camera/Digital camera) with an ambient light source. The study includes information about signal processing algorithms for estimating heart rate, relevant parameters, and comparison with standard techniques. Such low-cost, multi-purpose solutions for quick screening of subjects provides us with sensible and useful information on elevated body temperature and heart rate. Hence, these methods show promising results that enable mass fever screening as a possibility through temperature and heart rate monitoring, where low cost of installation and flexibility are important

Application of multispectral radar and LANDSAT imagery to geologic mapping in death valley

Side-Looking Airborne Radar (SLAR) images, acquired by JPL and Strategic Air Command Systems, and visible and near-infrared LANDSAT imagery were applied to studies of the Quaternary alluvial and evaporite deposits in Death Valley, California. Unprocessed radar imagery revealed considerable variation in microwave backscatter, generally correlated with surface roughness. For Death Valley, LANDSAT imagery is of limited value in discriminating the Quaternary units except for alluvial units distinguishable by presence or absence of desert varnish or evaporite units whose extremely rough surfaces are strongly shadowed. In contrast, radar returns are most strongly dependent on surface roughness, a property more strongly correlated with surficial geology than is surface chemistry

A preliminary training guide for utilizing high-altitude, color-infrared photography in compiling soil maps

Instruction for acquiring and analytically processing small-scale color-infrared photography to perform a soil resources inventory over forests of the southern U.S. is provided. Planning the project; acquiring aerial photography, materials, equipment and supplemental data; and preparing the photography for analysis are discussed. The procedures for preparing ancillary and primary component overlays are discussed. The use of correlation charts and dichotomous keys for mountain landforms, water regime, and vegetation is explained

Preoperative Systems for Computer Aided Diagnosis based on Image Registration: Applications to Breast Cancer and Atherosclerosis

Computer Aided Diagnosis (CAD) systems assist clinicians including radiologists and cardiologists to detect abnormalities and highlight conspicuous possible disease. Implementing a pre-operative CAD system contains a framework that accepts related technical as well as clinical parameters as input by analyzing the predefined method and demonstrates the prospective output. In this work we developed the Computer Aided Diagnostic System for biomedical imaging analysis of two applications on Breast Cancer and Atherosclerosis. The aim of the first CAD application is to optimize the registration strategy specifically for Breast Dynamic Infrared Imaging and to make it user-independent. Base on the fact that automated motion reduction in dynamic infrared imaging is on demand in clinical applications, since movement disarranges time-temperature series of each pixel, thus originating thermal artifacts that might bias the clinical decision. All previously proposed registration methods are feature based algorithms requiring manual intervention. We implemented and evaluated 3 different 3D time-series registration methods: 1. Linear affine, 2. Non-linear Bspline, 3. Demons applied to 12 datasets of healthy breast thermal images. The results are evaluated through normalized mutual information with average values of 0.70±0.03, 0.74±0.03 and 0.81±0.09 (out of 1) for Affine, BSpline and Demons registration, respectively, as well as breast boundary overlap and Jacobian determinant of the deformation field. The statistical analysis of the results showed that symmetric diffeomorphic Demons registration method outperforms also with the best breast alignment and non-negative Jacobian values which guarantee image similarity and anatomical consistency of the transformation, due to homologous forces enforcing the pixel geometric disparities to be shortened on all the frames. We propose Demons registration as an effective technique for time-series dynamic infrared registration, to stabilize the local temperature oscillation. The aim of the second implemented CAD application is to assess contribution of calcification in plaque vulnerability and wall rupture and to find its maximum resistance before break in image-based models of carotid artery stenting. The role of calcification inside fibroatheroma during carotid artery stenting operation is controversial in which cardiologists face two major problems during the placement: (i) “plaque protrusion” (i.e. elastic fibrous caps containing early calcifications that penetrate inside the stent); (ii) “plaque vulnerability” (i.e. stiff plaques with advanced calcifications that break the arterial wall or stent). Finite Element Analysis was used to simulate the balloon and stent expansion as a preoperative patient-specific virtual framework. A nonlinear static structural analysis was performed on 20 patients acquired using in vivo MDCT angiography. The Agatston Calcium score was obtained for each patient and subject-specific local Elastic Modulus (EM) was calculated. The in silico results showed that by imposing average ultimate external load of 1.1MPa and 2.3MPa on balloon and stent respectively, average ultimate stress of 55.7±41.2kPa and 171±41.2kPa are obtained on calcifications. The study reveals that a significant positive correlation (R=0.85, p<0.0001) exists on stent expansion between EM of calcification and ultimate stress as well as Plaque Wall Stress (PWS) (R=0.92, p<0.0001), comparing to Ca score that showed insignificant associations with ultimate stress (R=0.44, p=0.057) and PWS (R=0.38, p=0.103), suggesting minor impact of Ca score in plaque rupture. These average data are in good agreement with results obtained by other research groups and we believe this approach enriches the arsenal of tools available for pre-operative prediction of carotid artery stenting procedure in the presence of calcified plaques

Sharpening ECOSTRESS and VIIRS Land Surface Temperature Using Harmonized Landsat-Sentinel Surface Reflectances

Land surface temperature (LST) is a key diagnostic indicator of agricultural water use and crop stress. LST data retrieved from thermal infrared (TIR) band imagery, however, tend to have a coarser spatial resolution (e.g., 100 m for Landsat 8) than surface reflectance (SR) data collected from shortwave bands on the same instrument (e.g., 30 m for Landsat). Spatial sharpening of LST data using the higher resolution multi-band SR data provides an important path for improved agricultural monitoring at sub-field scales. A previously developed Data Mining Sharpener (DMS) approach has shown great potential in the sharpening of Landsat LST using Landsat SR data co-collected over various landscapes. This work evaluates DMS performance for sharpening ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LST (~70 m native resolution) and Visible Infrared Imaging Radiometer Suite (VIIRS) LST (375 m) data using Harmonized Landsat and Sentinel-2 (HLS) SR data, providing the basis for generating 30-m LST data at a higher temporal frequency than afforded by Landsat alone. To account for the misalignment between ECOSTRESS/VIIRS and Landsat/HLS caused by errors in registration and orthorectification, we propose a modified version of the DMS approach that employs a relaxed box size for energy conservation (EC). Sharpening experiments were conducted over three study sites in California, and results were evaluated visually and quantitatively against LST data from unmanned aerial vehicles (UAV) flights and from Landsat 8. Over the three sites, the modified DMS technique showed improved sharpening accuracy over the standard DMS for both ECOSTRESS and VIIRS, suggesting the effectiveness of relaxing EC box in relieving misalignment-induced errors. To achieve reasonable accuracy while minimizing loss of spatial detail due to the EC box size increase, an optimal EC box size of 180–270 m was identified for ECOSTRESS and about 780 m for VIIRS data based on experiments from the three sites. Results from this work will facilitate the development of a prototype system that generates high spatiotemporal resolution LST products for improved agricultural water use monitoring by synthesizing multi-source remote sensing data

Cloud detection based on high resolution stereo pairs of the geostationary meteosat images

Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future

Toward Global Localization of Unmanned Aircraft Systems using Overhead Image Registration with Deep Learning Convolutional Neural Networks

Global localization, in which an unmanned aircraft system (UAS) estimates its unknown current location without access to its take-off location or other locational data from its flight path, is a challenging problem. This research brings together aspects from the remote sensing, geoinformatics, and machine learning disciplines by framing the global localization problem as a geospatial image registration problem in which overhead aerial and satellite imagery serve as a proxy for UAS imagery. A literature review is conducted covering the use of deep learning convolutional neural networks (DLCNN) with global localization and other related geospatial imagery applications. Differences between geospatial imagery taken from the overhead perspective and terrestrial imagery are discussed, as well as difficulties in using geospatial overhead imagery for image registration due to a lack of suitable machine learning datasets. Geospatial analysis is conducted to identify suitable areas for future UAS imagery collection. One of these areas, Jerusalem northeast (JNE) is selected as the area of interest (AOI) for this research. Multi-modal, multi-temporal, and multi-resolution geospatial overhead imagery is aggregated from a variety of publicly available sources and processed to create a controlled image dataset called Jerusalem northeast rural controlled imagery (JNE RCI). JNE RCI is tested with handcrafted feature-based methods SURF and SIFT and a non-handcrafted feature-based pre-trained fine-tuned VGG-16 DLCNN on coarse-grained image registration. Both handcrafted and non-handcrafted feature based methods had difficulty with the coarse-grained registration process. The format of JNE RCI is determined to be unsuitable for the coarse-grained registration process with DLCNNs and the process to create a new supervised machine learning dataset, Jerusalem northeast machine learning (JNE ML) is covered in detail. A multi-resolution grid based approach is used, where each grid cell ID is treated as the supervised training label for that respective resolution. Pre-trained fine-tuned VGG-16 DLCNNs, two custom architecture two-channel DLCNNs, and a custom chain DLCNN are trained on JNE ML for each spatial resolution of subimages in the dataset. All DLCNNs used could more accurately coarsely register the JNE ML subimages compared to the pre-trained fine-tuned VGG-16 DLCNN on JNE RCI. This shows the process for creating JNE ML is valid and is suitable for using machine learning with the coarse-grained registration problem. All custom architecture two-channel DLCNNs and the custom chain DLCNN were able to more accurately coarsely register the JNE ML subimages compared to the fine-tuned pre-trained VGG-16 approach. Both the two-channel custom DLCNNs and the chain DLCNN were able to generalize well to new imagery that these networks had not previously trained on. Through the contributions of this research, a foundation is laid for future work to be conducted on the UAS global localization problem within the rural forested JNE AOI