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Articles 1 - 29 of 29
Full-Text Articles in Remote Sensing
A Survey Of Sampling Methods For Hyperspectral Remote Sensing: Addressing Bias Induced By Random Sampling, Kevin T. Decker, Brett J. Borghetti
A Survey Of Sampling Methods For Hyperspectral Remote Sensing: Addressing Bias Induced By Random Sampling, Kevin T. Decker, Brett J. Borghetti
Faculty Publications
Identified as early as 2000, the challenges involved in developing and assessing remote sensing models with small datasets remain, with one key issue persisting: the misuse of random sampling to generate training and testing data. This practice often introduces a high degree of correlation between the sets, leading to an overestimation of model generalizability. Despite the early recognition of this problem, few researchers have investigated its nuances or developed effective sampling techniques to address it. Our survey highlights that mitigation strategies to reduce this bias remain underutilized in practice, distorting the interpretation and comparison of results across the field. In …
Simulating The Impact Of Self-Sensing Materials On Aircraft Sortie Generation, Harmoni J. Blackstock
Simulating The Impact Of Self-Sensing Materials On Aircraft Sortie Generation, Harmoni J. Blackstock
Theses and Dissertations
In conjunction with the Air Force Research Laboratory Materials Lab(AFRL-RX), this study evaluates the potential military value of the prototype material sensing composites on Unmanned Aerial Vehicle (UAV) operations in intelligence, surveillance, reconnaissance (ISR), and close air support (CAS) missions within a contested Indo-Pacific theater. Using a Simio based simulation,UAV performance was assessed under varying combat conditions, focusing on Remote Sensing, deployment strategies, initial lay-downs, and varying loss rates. Re-sults show that UAVs equipped with Remote Sensing technology significantly improved sortie generation and logistical efficiency. Scenario 17 achieved the highest sortie rate(965.5 sorties), outperforming the next-best scenario by 25 sorties. …
Aerospace Vehicle Comprising Module For Method Of Terrain, Terrain Activity And Material Classification, Matthew E. Nussbaum, Marissa S. Herron
Aerospace Vehicle Comprising Module For Method Of Terrain, Terrain Activity And Material Classification, Matthew E. Nussbaum, Marissa S. Herron
AFIT Patents
A method of classifying terrain, terrain activity and materials through panchromatic imagery a module programmed to provide such classification and aerospace vehicles comprising such module is provided. Panchromatic images of known materials terrains and terrain activities are taken and processed to form a multiband texture cube, that due it amount of data, is stored as a computer data base. New panchromatic images of unclassified materials, terrains and/or terrain activities are processed and compared via computer with such database that allows for inexpensive, quick and efficient classification of such new images.
Deterministic Global 3d Fractal Cloud Model For Synthetic Scene Generation, Aaron M. Schinder, Shannon R. Young, Bryan J. Steward, Michael L. Dexter, Andrew Kondrath, Stephen Hinton, Ricardo Davila
Deterministic Global 3d Fractal Cloud Model For Synthetic Scene Generation, Aaron M. Schinder, Shannon R. Young, Bryan J. Steward, Michael L. Dexter, Andrew Kondrath, Stephen Hinton, Ricardo Davila
Faculty Publications
This paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal function. The fractal function is distorted by a displacement map, which is generated using horizontal wind data from a Global Forecast System (GFS) weather file. The vertical windspeed and relative humidity are used to mask the creation of clouds to match realistic large-scale weather patterns over the Earth. Small-scale detail is provided by the fractal functions which are tuned to …
Composite Style Pixel And Point Convolution-Based Deep Fusion Neural Network Architecture For The Semantic Segmentation Of Hyperspectral And Lidar Data, Kevin T. Decker, Brett J. Borghetti
Composite Style Pixel And Point Convolution-Based Deep Fusion Neural Network Architecture For The Semantic Segmentation Of Hyperspectral And Lidar Data, Kevin T. Decker, Brett J. Borghetti
Faculty Publications
Multimodal hyperspectral and lidar data sets provide complementary spectral and structural data. Joint processing and exploitation to produce semantically labeled pixel maps through semantic segmentation has proven useful for a variety of decision tasks. In this work, we identify two areas of improvement over previous approaches and present a proof of concept network implementing these improvements. First, rather than using a late fusion style architecture as in prior work, our approach implements a composite style fusion architecture to allow for the simultaneous generation of multimodal features and the learning of fused features during encoding. Second, our approach processes the higher …
Global Gnss-Ro Electron Density In The Lower Ionosphere, Dong L. Wu, Daniel J. Emmons Ii, Nimalan Swarnalingam
Global Gnss-Ro Electron Density In The Lower Ionosphere, Dong L. Wu, Daniel J. Emmons Ii, Nimalan Swarnalingam
Faculty Publications
Lack of instrument sensitivity to low electron density (Ne) concentration makes it difficult to measure sharp Ne vertical gradients (four orders of magnitude over 30 km) in the D/E-region. A robust algorithm is developed to retrieve global D/E-region Ne from the high-rate GNSS radio occultation (RO) data, to improve spatiotemporal coverage using recent SmallSat/CubeSat constellations. The new algorithm removes F-region contributions in the RO excess phase profile by fitting a linear function to the data below the D-region. The new GNSS-RO observations reveal many interesting features in the diurnal, seasonal, solar-cycle, and magnetic-field-dependent variations in the …
A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons
A Comparison Of Sporadic-E Occurrence Rates Using Gps Radio Occultation And Ionosonde Measurements, Rodney Carmona, Omar A. Nava, Eugene V. Dao, Daniel J. Emmons
Faculty Publications
Sporadic-E (Es) occurrence rates from Global Position Satellite radio occultation (GPS-RO) measurements have shown to vary by a factor of five between studies, motivating the need for a comparison with ground-based measurements. In an attempt to find accurate GPS-RO techniques for detecting Es formation, occurrence rates derived using five previously developed GPS-RO techniques are compared to ionosonde measurements over an eight-year period from 2010–2017. GPS-RO measurements within 170 km of a ionosonde site are used to calculate Es occurrence rates and compared to the ground-truth ionosonde measurements. The techniques are compared individually for each ionosonde site …
Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner, Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Machine Learning Land Cover And Land Use Classification Of 4-Band Satellite Imagery, Lorelei Turner, Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Faculty Publications
Land-cover and land-use classification generates categories of terrestrial features, such as water or trees, which can be used to track how land is used. This work applies classical, ensemble and neural network machine learning algorithms to a multispectral remote sensing dataset containing 405,000 28x28 pixel image patches in 4 electromagnetic frequency bands. For each algorithm, model metrics and prediction execution time were evaluated, resulting in two families of models; fast and precise. The prediction time for an 81,000-patch group of predictions wasmodels, and >5s for the precise models, and there was not a significant change in prediction time when a …
Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco, Torrey J. Wagner, Brent T. Langhals
Per-Pixel Cloud Cover Classification Of Multispectral Landsat-8 Data, Salome E. Carrasco, Torrey J. Wagner, Brent T. Langhals
Faculty Publications
Random forest and neural network algorithms are applied to identify cloud cover using 10 of the wavelength bands available in Landsat 8 imagery. The methods classify each pixel into 4 different classes: clear, cloud shadow, light cloud, or cloud. The first method is based on a fully connected neural network with ten input neurons, two hidden layers of 8 and 10 neurons respectively, and a single-neuron output for each class. This type of model is considered with and without L2 regularization applied to the kernel weighting. The final model type is a random forest classifier created from an ensemble of …
Wideband Satcom Model: Evaluation Of Numerical Accuracy And Efficiency, Andrew J. Knisely, Andrew J. Terzuoli Jr.
Wideband Satcom Model: Evaluation Of Numerical Accuracy And Efficiency, Andrew J. Knisely, Andrew J. Terzuoli Jr.
Faculty Publications
The spectral method is typically applied as a simple and efficient method to solve the parabolic wave equation in phase screen scintillation models. The critical factors that can greatly affect the spectral method accuracy is the uniformity and smoothness of the input function. This paper observes these effects on the accuracy of the finite difference and the spectral methods applied to a wideband SATCOM signal propagation model simulated in the ultra-high frequency (UHF) band. The finite difference method uses local pointwise approximations to calculate a derivative. The spectral method uses global trigonometric interpolants that achieve remarkable accuracy for continuously differentiable …
Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing, Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola
Learning Set Representations For Lwir In-Scene Atmospheric Compensation, Nicholas M. Westing, Kevin C. Gross, Brett J. Borghetti, Jacob A. Martin, Joseph Meola
Faculty Publications
Atmospheric compensation of long-wave infrared (LWIR) hyperspectral imagery is investigated in this article using set representations learned by a neural network. This approach relies on synthetic at-sensor radiance data derived from collected radiosondes and a diverse database of measured emissivity spectra sampled at a range of surface temperatures. The network loss function relies on LWIR radiative transfer equations to update model parameters. Atmospheric predictions are made on a set of diverse pixels extracted from the scene, without knowledge of blackbody pixels or pixel temperatures. The network architecture utilizes permutation-invariant layers to predict a set representation, similar to the work performed …
Mult-Spectral Imaging Of Vegetation With A Diffractive Plenoptic Camera, Tristan R. Naranjo
Mult-Spectral Imaging Of Vegetation With A Diffractive Plenoptic Camera, Tristan R. Naranjo
Theses and Dissertations
Snapshot multi-spectral sensors allow for object detection based on its spectrum for remote sensing applications in air or space. By making these types of sensors more compact and lightweight, it allows drones to dwell longer on targets or the reduction of transport costs for satellites. To address this need, I designed and built a diffractive plenoptic camera (DPC) which utilized a Fresnel zone plate and a light field camera in order to detect vegetation via a normalized difference vegetation index (NDVI). This thesis derives design equations by relating DPC system parameters to its expected performance and evaluates its multi-spectral performance. …
Multi-Sensor Data Fusion Between Radio Tomographic Imaging And Noise Radar, Christopher Vergara
Multi-Sensor Data Fusion Between Radio Tomographic Imaging And Noise Radar, Christopher Vergara
Theses and Dissertations
The lack of situational awareness within an operational environment is a problem that carries high risk and expensive consequences. Radio Tomographic Imaging (RTI) and noise radar are two proven technologies capable of through-wall imaging and foliage penetration. The intent of this thesis is to provide a proof of concept for the fusion of data from RTI and noise radar. The output of this thesis will consist of a performance comparison between the two technologies followed by the derivation of a fusion technique to produce a single image. Proposals have been made for the integration of multiple-input multiple-output (MIMO) radar with …
Remote Sensing Of Hidden Objects, Mark G. Hoelscher, Michael A. Marciniak
Remote Sensing Of Hidden Objects, Mark G. Hoelscher, Michael A. Marciniak
AFIT Patents
An apparatus and method are provided for creating an indirect image of an object. The apparatus includes a light source and an imaging system. Light emitted from the light source is reflected by a first non-specular surface toward the object. Light reflected by the object is further reflected by a second non-specular surface toward the imaging system. The imaging system is configured to create the indirect image from the reflected light.
Automated Synthetic Scene Generation, Ryan N. Givens
Automated Synthetic Scene Generation, Ryan N. Givens
Theses and Dissertations
First principles, physics-based models help organizations developing new remote sensing instruments anticipate sensor performance by enabling the ability to create synthetic imagery for proposed sensor before a sensor is built. One of the largest challenges in modeling realistic synthetic imagery, however, is generating the spectrally attributed, three-dimensional scenes on which the models are based in a timely and affordable fashion. Additionally, manual and semi-automated approaches to synthetic scene construction which rely on spectral libraries may not adequately capture the spectral variability of real-world sites especially when the libraries consist of measurements made in other locations or in a lab. This …
Polarimetric Calibration And Characterization Of The Telops Field Portable Polarimetric-Hyperspectral Imager, Joel G. Holder
Polarimetric Calibration And Characterization Of The Telops Field Portable Polarimetric-Hyperspectral Imager, Joel G. Holder
Theses and Dissertations
The Telops polarimetric-hyperspectral imager combines polarimetric and hyperspectral technologies to enable enhanced scene characterization. The Defense Threat Reduction Agency funded research at AFIT to leverage this capability to provide more accurate scene information to radiation transport models that will allow for more effective location of radiation sources within a region of interest. To support the objectives of the DTRA effort, there is a requirement for highly accurate radiometric, polarimetric, and spectral data on a pixel-by-pixel basis. The complex nature of the Telops instrument combined with working in the thermal IR waveband makes achieving this accuracy a challenge. This thesis develops …
Design Of A Monocular Multi-Spectral Skin Detection, Melanin Estimation, And False-Alarm Suppression System, Keith R. Peskosky
Design Of A Monocular Multi-Spectral Skin Detection, Melanin Estimation, And False-Alarm Suppression System, Keith R. Peskosky
Theses and Dissertations
A real-time skin detection, false-alarm reduction, and melanin estimation system is designed targeting search and rescue (SAR) with application to special operations for manhunting and human measurement and signatures intelligence. A mathematical model of the system is developed and used to determine how the physical system performs under illumination, target-to-sensor distance, and target-type scenarios. This aspect is important to the SAR community to gain an understanding of the deployability in different operating environments. A multi-spectral approach is developed and consists of two short-wave infrared cameras and two visible cameras. Through an optical chain of lenses, custom designed and fabricated dichroic …
Using Multiple Robust Parameter Design Techniques To Improve Hyperspectral Anomaly Detection Algorithm Performance, Matthew T. Davis
Using Multiple Robust Parameter Design Techniques To Improve Hyperspectral Anomaly Detection Algorithm Performance, Matthew T. Davis
Theses and Dissertations
Detecting and identifying objects of interest is the goal of all remote sensing. New advances, specifically in hyperspectral imaging technology have provided the analyst with immense amounts of data requiring evaluation. Several filtering techniques or anomaly detection algorithms have been proposed. However, most new algorithms are insufficiently verified to be robust to the broad range of hyperspectral data being made available. One such algorithm, AutoGAD, is tested here via two separate robust parameter design techniques to determine optimal parameters for consistent performance on a range of data with large attribute variances. Additionally, the results of the two techniques are compared …
Satellite-Based Fusion Of Image/Inertial Sensors For Precise Geolocation, Neil R. Jesse
Satellite-Based Fusion Of Image/Inertial Sensors For Precise Geolocation, Neil R. Jesse
Theses and Dissertations
The ability to produce high-resolution images of the Earth’s surface from space has flourished in recent years with the continuous development and improvement of satellite-based imaging sensors. Earth-imaging satellites often rely on complex onboard navigation systems, with dependence on Global Positioning System (GPS) tracking and/or continuous post-capture georegistration, to accurately geolocate ground targets of interest to either commercial and military customers. Consequently, these satellite systems are often massive, expensive, and susceptible to poor or unavailable target tracking capabilities in GPS-denied environments. Previous research has demonstrated that a tightly-coupled image-aided inertial navigation system (INS), using existing onboard imaging sensors, can provide …
Statistical Methods For Polarimetric Imagery, Daniel A. Lemaster
Statistical Methods For Polarimetric Imagery, Daniel A. Lemaster
Theses and Dissertations
Estimation theory is applied to a physical model of incoherent polarized light to address problems in polarimetric image registration, restoration, and analysis for electro-optical imaging systems. In the image registration case, the Cramer-Rao lower bound on unbiased joint estimates of the registration parameters and the underlying scene is derived, simplified using matrix methods, and used to explain the behavior of multi-channel linear polarimetric imagers. In the image restoration case, a polarimetric maximum likelihood blind deconvolution algorithm is derived and tested using laboratory and simulated imagery. Finally, a principal components analysis is derived for polarization imaging systems. This analysis expands upon …
Dynamics And Control Of Tethered Satellite Formations For The Purpose Of Space-Based Remote Sensing, Kurt A. Vogel
Dynamics And Control Of Tethered Satellite Formations For The Purpose Of Space-Based Remote Sensing, Kurt A. Vogel
Theses and Dissertations
This dissertation assesses the utility of tethered satellite formations for the space-based remote sensing mission. Energy dissipation is found to have an adverse effect on foundational rigid body (Likins-Pringle) equilibria. It is shown that a continuously earth-facing equilibrium condition for a fixed-length tethered system does not exist since the spin rate required for the proper precession would not be high enough to maintain tether tension. The range of required spin rates for steady-spin motion is numerically defined here, but none of these conditions can meet the continuously earth-facing criteria. Of particular note is the discovery that applying certain rigid body …
Terrain And Spatial Effects On Hazard Prediction And Assessment Capability (Hpac) Software Dose-Rate Contour Plot Predictions As Compared To A Sample Of Local Fallout Data From Test Detonations In The Continental United States, 1945-1962, Kevin D. Pace
Theses and Dissertations
Hazard Prediction and Capability (HPAC) Software is validated by comparing modeled predictions to historical test data. Reanalysis weather data is acquired and reformatted for use in HPAC. Simulations are made using various amounts of weather data by use of a spatial domain. Simulations are also varied by levels of terrain resolution. The predicted output of the software is numerically compared to historical test data. The result of this research culminated in the knowledge that HPAC prediction accuracy is improved by using terrain resolutions beyond the flat earth assumption. Furthermore, this research establishes that domain size variation produces no significant advantage …
An Estimation Theory Approach To Detection And Ranging Of Obscured Targets In 3-D Ladar Data, Charles R. Burris
An Estimation Theory Approach To Detection And Ranging Of Obscured Targets In 3-D Ladar Data, Charles R. Burris
Theses and Dissertations
The purpose of this research is to develop an algorithm to detect obscured images in 3-D LADAR data. The real data used for this research was gathered using a FLASH LADAR system under development at AFRL/SNJM. The system transmits light with a wavelength of 1.55 micrometers and produces 20 128 X 128 temporally resolved images from the return pulse separated by less than 2 nanoseconds in time. New algorithms for estimating the range to a target in 3-D FLASH LADAR data were developed. Results from processing real data are presented and compared to the traditional correlation receiver for extracting ranges …
Modeling And Simulation Of Commercial Satellite Imagery Processes, David A. Shultz
Modeling And Simulation Of Commercial Satellite Imagery Processes, David A. Shultz
Theses and Dissertations
The purpose of this research was to develop a general, statistical model of order-to-delivery times for commercial satellite imagery. The research looked at the current four satellite providers with 3-meter or better imagers in the context of a generalized model of commercial imaging satellite operations. Existing methods use orbit analysis tools to determine the imaging time of a specified target based on defined satellite position and times, but can only develop shortest and longest times to an imaging opportunity. To address the general question of the time it takes to deliver an image for non-specific targets, this research develops a …
Simulating A Chromotomographic Sensor For Hyperspectral Imaging In The Infrared, Anthony J. Dearinger
Simulating A Chromotomographic Sensor For Hyperspectral Imaging In The Infrared, Anthony J. Dearinger
Theses and Dissertations
Hyperspectral imaging systems passively sense radiant electromagnetic energy from a remote scene to form a three dimension profile of the remote scene. The data contained in this profile describes real images of the remote scene for a certain number of spectral wavelength bands across a finite spectral range of electromagnetic radiation. Typical grating type hyperspectral imaging systems collect spectral electromagnetic radiation in the visible and near infrared spectral range, by incrementally scanning across the spatial extent of the remote scene. The legacy of low optical throughput because of the optical scanning techniques employed in these systems means adapting these systems …
Commercial Regional Space/Airborne Imaging, Ugur Akyazi, Ali Durmus, Birce Boga Bakirli, Arif Arin
Commercial Regional Space/Airborne Imaging, Ugur Akyazi, Ali Durmus, Birce Boga Bakirli, Arif Arin
Theses and Dissertations
In this work goal programming is used to solve a minimum cost multicommodity network flow problem with multiple goals. A single telecommunication network with multiple commodities (e.g., voice, video, data, etc.) flowing over it is analyzed. This network consists of: linear objective function, linear cost arcs, fixed capacities, specific origin-destination pairs for each commodity. A multicommodity network flow problem with goals can be successfully modeled using linear goal programming techniques. When properly modeled, network flow techniques may be employed to exploit the pure network structure of a multicommodity network flow problem with goals. Lagrangian relaxation captures the essence of the …
Analysis Of Gps Satellite Allocation For The United States Nuclear Detonation Detection System (Usnds), Aaron J. Bell
Analysis Of Gps Satellite Allocation For The United States Nuclear Detonation Detection System (Usnds), Aaron J. Bell
Theses and Dissertations
We present an approach for identifying salient input features in high feature to exemplar ratio conditions. Basically we modify the SNR saliency-screening algorithm to improve the solution of the optimal salient feature subset problem. We propose that applying the SNR method to randomly selected subsets (SRSS) has a superior potential to identify the salient features than the traditional SNR algorithm has. Two experimental studies are provided to demonstrate the consistency of the SRSS. In the first experiment we used a noise-corrupted version of the Fisher s Iris classification problem. The first experiment designed to prove the fidelity of the SRSS …
Collection Of Detonation Signatures And Characterization Of Spectral Features, Jay A. Orson
Collection Of Detonation Signatures And Characterization Of Spectral Features, Jay A. Orson
Theses and Dissertations
The remote observations of the temporal and spectral characteristics of the infrared (IR) emissions from exploding ordnance have been correlated with explosion conditions. A Bomem MR-154 Fourier Transform Interferometer with two detectors, InSb and HgCdTe, and a 75 mrad telescope was used to record spectra in the 1.3 - 20 µm range. Data was collected at spectral resolutions of 16 cm-1 and 4 cm-1 and temporal resolutions of 0.045 s and 0.123 s respectively. Two field tests were conducted: (1) a set of dynamic drop explosions and (2) a series of static detonations. In most events, the temporal …
Binary Programming Models Of Spatial Pattern Recognition: Applications In Remote Sensing Image Analysis, Thomas G. Reed
Binary Programming Models Of Spatial Pattern Recognition: Applications In Remote Sensing Image Analysis, Thomas G. Reed
Theses and Dissertations
The major purpose of this investigation was to implement subregion allocation objectives using a network model base on an existing subregion allocation binary programming model (Benabdallah and Wright (B&W), 1990), the ultimate goal being the application of subregion allocation concepts towards the spatial analysis of satellite imagery. The multi-objective aspects of subregion allocation can be accomplished via a network formulation, a formulation vastly simpler in complexity than the binary programming models previously used. Without a network programming package that could maintain integral flows, however, deriving the solution was a tiresome task for the user. Nonetheless, several new concepts and advantages …