Hyperspectral sensing is a valuable tool for detecting anomalies and distinguishing between materials in a scene. Hyperspectral anomaly detection (HS-AD) helps characterize the captured scenes and separates them into anomaly and background classes. It is vital in agriculture, environment, and military applications such as RSTA (reconnaissance, surveillance, and target acquisition) missions. We previously designed an equal voting ensemble of hyperspectral unmixing and three unsupervised HS-AD algorithms. We later utilized a supervised classifier to determine the weights of a voting ensemble, creating a hybrid of heterogeneous unsupervised HS-AD algorithms with a supervised classifier in a model stacking, which improved detection accuracy. However, supervised classification methods usually fail to detect novel or unknown patterns that substantially deviate from those seen previously. In this work, we evaluate our technique and other supervised and unsupervised methods using general hyperspectral data to provide new insights.
Research supporting improved anomaly detection performance benefits a wide range of technical applications, and thus, the definition of what anomalies are and the subsequent means to detect them are wide ranging. In this treatment, an overview of the development of an anomaly detection approach based on spectral signatures obtained with hyperspectral unmixing is presented. The algorithm is designed to address some of the shortcomings of current techniques whose functionality is dependent upon normalized differences between discrete frequencies or spectral components, or those based on estimated distances between background spectra and pixels under test. Details about the extracted endmembers and their use for effective anomaly detection will be presented as well as, some thoughts on the expected requirements for future machine learning based implementations.
The Reed-Xiaoli Detection (RX) algorithm is a classic algorithm commonly used to detect anomalies in hyperspectral image data, i.e. regions which are spectrally distinct from the image background. Such regions may represent interesting objects to human observers. We investigate the possibility of applying the RX algorithm to a VNIR pushbroom hyperspectral image sensor in real time onboard a small uncrewed aerial system (UAS). The generated anomaly information is much more concise and can be transmitted much faster than the raw hyperspectral data. This would enable anomalies to be automatically detected, then communicated to a ground station for immediate attention by a human observer. However, the UAS payload capacities impose strict size, weight, and power constraints. We show in what contexts the algorithm can be successfully applied and how the UAS constraints bound algorithm performance and parameters.
In phase-shifting digital holographic microscopy (PS-DHM), the reconstructed phase map is obtained after processing several holograms of the same scene with a phase shift between them. Most reconstruction algorithms in PS-DHM require an accurate and known phase shift between the holograms, requirement that limits the PS-DHM applicability. This work presents an iterative-blind phase shift extraction method based on the demodulation of the different components of the holograms using three-frame holograms with arbitrary and unequal phase-shifts. Both simulated and experimental results demonstrate the goodness and feasibility of the proposed technique.
KEYWORDS: Digital signal processing, Principal component analysis, Signal processing, Laser induced breakdown spectroscopy, Distance measurement, Spectroscopy, Binary data, Sensors, Sensor technology, Data analysis
There are many accepted sensor technologies for generating spectra for material classification. Once the spectra are
generated, communication bandwidth limitations favor local material classification with its attendant reduction in data
transfer rates and power consumption. Transferring sensor technologies such as Cavity Ring-Down Spectroscopy
(CRDS) and Laser Induced Breakdown Spectroscopy (LIBS) require effective material classifiers. A result of recent
efforts has been emphasis on Partial Least Squares - Discriminant Analysis (PLS-DA) and Principle Component
Analysis (PCA). Implementation of these via general purpose computers is difficult in small portable sensor
configurations. This paper addresses the creation of a low mass, low power, robust hardware spectra classifier for a
limited set of predetermined materials in an atmospheric matrix. Crucial to this is the incorporation of PCA or PLS-DA
classifiers into a predictor-corrector style implementation. The system configuration guarantees rapid convergence.
Software running on multi-core Digital Signal Processor (DSPs) simulates a stream-lined plasma physics model
estimator, reducing Analog-to-Digital (ADC) power requirements. This paper presents the results of a predictorcorrector
model implemented on a low power multi-core DSP to perform substance classification. This configuration
emphasizes the hardware system and software design via a predictor corrector model that simultaneously decreases the
sample rate while performing the classification.
Turbulence mitigation techniques require input data representing a wide variety of turbulent atmospheric
and weather conditions in order to produce robust results and wider ranges of applicability. In the past, this
has implied the need for numerous data collection equipment items to account for multiple frequency bands
and various system configurations. However, recent advancements in turbulence simulation techniques
have resulted in viable options to real-time data collection with various levels of available simulation
accuracy. This treatment will detail the development and implementation of an extension to the second
order statistical turbulence simulation model presented by Repasi1 and others. The Repasi model is
extended to include the effects of various wavelengths, optical configurations, and short exposure imaging
on angle of arrival fluctuation statistics. The result of the development is an atmospheric turbulence
simulation technique that is physics-based but less computationally intensive than phase-based or deflector
screen approaches. In these cases, the statistical approach detailed in this paper provides the user with an
opportunity to obtain a better trade-off between accuracy and simulation run-time. The mathematical
development and reasoning behind the changes to the previous statistical model will be presented, and
sample imagery produced by the extended technique will be included. The result is a model that captures
the major turbulence effects required for algorithm development for large classes of mitigation techniques.
Wireless sensor networks (WSN) have become powerful tools for gathering and monitoring environmental data. These
networking systems can be utilized for many different applications due to their autonomy, ability to withstand harsh
conditions, and the reduced cost associated with their collection of data. These characteristics are beneficial across a
wide range of applications including those specific to the military, environmental, industrial, and medical industries.
Additionally, they become increasingly more relevant in remote sensing applications where size weight and power
trade-offs are of particular importance. Conversely, these applications also demonstrate the Achilles heel of a large
percentage of WSNs in that they run on limited power sources. Thus, energy efficiency is a major concern and therefore
a significant amount of research has been dedicated to identifying methods of making WSNs as energy efficient as
possible. The purpose of this paper is to detail a reactive wireless sensor network protocol that will minimize network
overhead and energy consumption in an effort to provide longevity to the overall network. The underlying components
of the Sensor-Triggered Efficient Routing protocol, STER, will be covered and the asynchronous handshaking method
used to transmit data between the sending and receiving nodes will also be described. The power consumption performance results of STER will then be compared to those obtained from other protocols in the current literature. The data will show that implementation of the STER protocol should result in a wireless sensor network with an increased life span.
KEYWORDS: Sensors, Systems modeling, Profiling, Target detection, Data modeling, Atmospheric modeling, Performance modeling, Target recognition, Animal model studies, Analytical research
This paper details the continued development of a modularized system level model of a sparse detector
sensor system. The assumptions used to simplify the equations describing the effects of individual system
components and characteristics such as target to background properties, collection optics, detectors, and
classifiers will be detailed and modeled. These individual effects will then be combined to provide an
overall system performance model and used to compare two sensor node designs.
The model will facilitate design trade offs for Unattended Ground Sensors. The size and power restrictions
of these sensors often preclude these sensors from being effective in high-resolution applications such as
target identification. However, these systems are well suited for applications such as broad scale
classifications or differentiations between targets such as humans, animals or small vehicles. Therefore, the
demand for these sensors is increasing for both the military and homeland security.
This paper details the development, experimentation, collected data and the results of research designed to gain an
understanding of the effects of clutter on the temporal and spatial image collection guidelines for tracking urban vehicles.
More specifically, a quantitative understanding of the relationship between human observer performance and the spatial
and temporal resolution is sought. Performance is measured as a function of the number of video frames per second,
imager spatial resolution and the ability of the observer to accurately determine the destination of a moving vehicle target
as it encounters vehicles with similar infrared signatures. The research is restricted to data and imagery collected from
altitudes typical of modern low to mid altitude persistent surveillance platforms using a wide field of view. The ability
of the human observer to perform an unaided track of the vehicle was determined by their completion of carefully
designed perception experiments. In these experiments, the observers were presented with simulated imagery from Night Vision's EOSim urban terrain simulator. The details of the simulated targets and backgrounds, the design of the experiments and their associated results are included in this treatment.
This paper details the development of a modularized system level model of a sensor
whose detector dimensions may be small with respect to the distance between adjacent
detectors. The effects of individual system components and characteristics such as target
to background properties, collection optics, detectors, and classifiers will be modeled.
These individual effects will then be combined to provide an overall system performance
model.
The model will facilitate design trade offs for Unattended Ground Sensors. The size and
power restrictions of these sensors often preclude these sensors from being effective in
high resolution applications such as target identification. Consequently, existing imager
performance models are not directly applicable. However, these systems are well suited
for applications such as broad scale classifications or differentiations between targets
such as humans, animals or small vehicles. Furthermore, these sensors do not have to be
spaced closely together to be effective in these applications. Therefore, the demand for
these sensors is increasing for both the military and homeland security.
Real MWIR Persistent Surveillance (PS) data was taken with a single human walking from a known point to different tents in the PS sensor field of view. The spatial resolution (ground sample distance) and revisit rate was varied from 0.5 to 2 meters and 1/8th to 4 Hz, respectively. A perception experiment was conducted where the observer was tasked to track the human to the terminal (end of route) tent. The probability of track is provided as a function of ground sample distance and revisit rate. These results can help determine PS design requirements for tracking and back-tracking humans on the ground. This paper begins with a summary of two previous simulation experiments: one for human tracking and one for vehicle tracking.
This paper details the development, experimentation, collected data and the results of research designed to gain an understanding of the temporal and spatial image collection guidelines for tracking urban vehicles. More specifically, a quantitative understanding of the relationship between human observer performance and the spatial and temporal resolution is sought. Performance is measured as a function of the number of video frames per second, imager spatial resolution and the ability of the observer to accurately determine the destination of a moving vehicle target. The research is restricted to data and imagery collected from altitudes typical of modern low to mid altitude persistent surveillance platforms using a wide field of view. The ability of the human observer to perform an unaided track of the vehicle was determined by their completion of carefully designed perception experiments. In these experiments, the observers were presented with simulated imagery from Night Vision's EOSim urban terrain simulator. The details of the simulated targets and backgrounds, the design of the experiments and their associated results are included in this treatment.
In this paper we estimate finite mixture models (FMM) to describe the statistics of the ultrawideband (UWB) channel
amplitudes. Various combinations of Rayleigh, Nakagami, Weibull, and Lognormal distributions are used to form the
constituent probability density functions (PDFs) of the FMMs. The FMMs are identified using the Stochastic
Expectation Maximization (SEM) algorithm. Akaike's Information Criterion is used to compare the quality of data fit
provided by the FMMs and models containing only one distribution (non-mixtures). The results indicate that UWB
channel amplitude statistics are best represented by mixtures of Rayleigh, Lognormal and Weibull PDFs.
Spectral-spatial independent component analysis (ICA) basis functions of visible color images are similar to some processing elements in the human visual systems in that they resemble Gabor filters and show color opponencies. In this research we studied combined spectral-spatial ICA basis functions of multispectral mid wave infrared (MWIR) images. These ICA spectral-spatial basis functions were then used as filters to extract features from multispectral MWIR images for classification. The images were captured in the 3.0–5.0 µm, 3.7–4.2 µm, and 4.0–4.5 µm bands using a multispectral MWIR camera. In the proposed algorithm, phase relationships between the basis functions indicate how the extracted features from the different spectral band images can be combined. We used classification performance to compare features obtained by filtering using multispectral ICA basis functions, multispectral principal component analysis basis functions, and Gabor filters.
We describe the development, experimentation, collected data, and results of research designed to gain an understanding of the temporal and spatial image collection guidelines for tracking humans. More specifically, we seek a quantitative understanding of the relationship between human observer performance and the spatial and temporal resolutions. We measure performance as a function of the number of video frames per second, the imager spatial resolution, and the ability of the observer to accurately determine the destination of a moving human target. Our research is restricted to data and imagery collected from typical modern, low- to mid-altitude, persistent surveillance platforms using a wide field of view. The ability of the human observer to track a human target unaided was determined by the observer's completion of carefully designed perception experiments. In these experiments, the observers were presented with simulated imagery from the U.S. Army Night Vision and Electronic Sensor Directorate's EOSim urban terrain simulator. The details of the simulated targets and backgrounds, and the design of the experiments as well as their associated results, are included in this treatment.
Accumulative Predictive Error has been previously used for time series modeling of psychological response time data.
In this paper we extend its applicabilty to the identification of tap amplitude statistics of ultrawideband communication
channels. We also present channel modeling results from two other model selection techniques: Minimum Description
Length and Akaike's Information Criterion. Channel models are also identified by hypothesis testing using
Kolmogorov-Smirnov test. The results agree with recent findings that Rayleigh distribution can still be used to model
tap amplitude statistics of line of sight ultrawideband communication channels.
An effective simulation of speckle with tilted surfaces illuminated by short-coherence-length lasers is presented. Two new tools for assessing speckle and/or its contrast under these conditions are developed and validated. The first is a simulation of the time-domain tilted-surface effects that provides speckle imagery. The second is a simple intuitive model for contrast derived from speckle reduction due to averaging. Field results of speckle imagery for a laser-illuminated target and a short-wave infrared imager validate the simulation. Simulated speckle is compared visually with the actual speckle. Also, contrasts of the speckle generated by simulation and actually imaged in the field are compared for one tilt angle. Existing analytical models of the contrast also validate the simulated speckle contrast. Contrast-versus-angle characteristics of simulated speckle are compared with the general analytical contrast model for speckle from tilted surfaces.
This paper details the development, experimentation, collected data and the results of research designed to gain an understanding of the temporal and spatial image collection guidelines for tracking humans. More specifically, a quantitative understanding of the relationship between human observer performance and the spatial and temporal resolution is sought. Performance is measured as a function of the number of video frames per second, imager spatial resolution and the ability of the observer to accurately determine the destination of a moving human target. The research is restricted to data and imagery collected from altitudes typical of modern low to mid altitude persistent surveillance platforms using a wide field of view. The ability of the human observer to perform an unaided track of a human target was determined by their completion of carefully designed perception experiments. In these experiments, the observers were presented with simulated imagery from Night Vision's EOSim urban terrain simulator. The details of the simulated targets and backgrounds, the design of the experiments and their associated results are included in this treatment.
It is known that spectral-spatial ICA basis functions of visible color images are similar to some processing elements in
the human visual systems in that they resemble Gabor filters and show color opponencies. In this research we study
combined spectral-spatial ICA basis functions of multispectral MWIR images. These ICA spectral-spatial basis
functions are then used as filters to extract features from multispectral MWIR images. It is hypothesized that learning
the added dimension of spectral information along with spatial characteristics of basis functions using ICA improves
classification performance for multispectral MWIR images. The images are captured in the 3.0 - 5.0um, 3.7 - 4.2um
and 4.0 - 4.5um bands using a multispectral MWIR camera. The phase relationship between the basis functions indicate
how the extracted features from the different spectral band images can be combined. We use classification performance
to compare features obtained by filtering using multispectral ICA basis functions, multispectral PCA basis functions and
opponent Gabor filters.
This study determines the effectiveness of a number of image fusion algorithms through the use of the following image metrics: mutual information, fusion quality index, weighted fusion quality index, edge-dependent fusion quality index and Mannos-Sakrison’s filter. The results obtained from this study provide objective comparisons between the algorithms. It is postulated that multi-spectral sensors enhance the probability of target discrimination through the additional information available from the multiple bands. The results indicate that more information is present in the fused image than either single band image. The image quality metrics quantify the benefits of fusion of MWIR and LWIR imagery.
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