We continue to highlight the pattern recognition challenges associated with solid target spectral variability in the longwave infrared (LWIR) region of the electromagnetic spectrum for a persistent imaging experiment. The experiment focused on the collection and exploitation of LWIR hyperspectral imagery. We propose two methods for target detection, one based on the repeated-random-sampling trial adaptation to a single-class version of support vector machine, and the other based on a longitudinal data model. The defining characteristic of a longitudinal study is that objects are measured repeatedly through time and, as a result, data are dependent. This is in contrast to cross-sectional studies in which the outcomes of a specific event are observed by randomly sampling from a large population of relevant objects in which data are assumed independent. Researchers in the remote sensing community generally assume the problem of object recognition to be cross-sectional. Performance contrast is quantified using a LWIR hyperspectral dataset acquired during three consecutive diurnal cycles, and results reinforce the need for using data models that are more realistic to LWIR spectral data.
We introduce an algorithm based on morphological filters with the Stokes parameters that augments the daytime and nighttime detection of weak-signal manmade objects immersed in a predominant natural background scene. The approach features a tailored sequence of signal-enhancing filters, consisting of core morphological operators (dilation, erosion) and higher level morphological operations (e.g., spatial gradient, opening, closing) to achieve a desired overarching goal. Using representative data from the SPICE database, the results show that the approach was able to automatically and persistently detect with a high confidence level the presence of three mobile military howitzer surrogates (targets) in natural clutter.
We give updates on a persistent imaging experiment dataset, being considered for public release in a foreseeable future, and present additional observations analyzing a subset of the dataset. The experiment is a long-term collaborative effort among the Army Research Laboratory, Army Armament RDEC, and Air Force Institute of Technology that focuses on the collection and exploitation of longwave infrared (LWIR) hyperspectral imagery. We emphasize the inherent challenges associated with using remotely sensed LWIR hyperspectral imagery for material recognition, and show that this data type violates key data assumptions conventionally used in the scientific community to develop detection/ID algorithms, i.e., normality, independence, identical distribution. We treat LWIR hyperspectral imagery as Longitudinal Data and aim at proposing a more realistic framework for material recognition as a function of spectral evolution through time, and discuss limitations. The defining characteristic of a longitudinal study is that objects are measured repeatedly through time and, as a result, data are dependent. This is in contrast to cross-sectional studies in which the outcomes of a specific event are observed by randomly sampling from a large population of relevant objects in which data are assumed independent. Researchers in the remote sensing community generally assume the problem of object recognition to be cross-sectional. But through a longitudinal analysis of a fixed site with multiple material types, we quantify and argue that, as data evolve through a full diurnal cycle, pattern recognition problems are longitudinal in nature and that by applying this knowledge may lead to better algorithms.
Our first observations using the longwave infrared (LWIR) hyperspectral data subset of the Spectral and Polarimetric Imagery Collection Experiment (SPICE) database are summarized in this paper, focusing on the inherent challenges associated with using this sensing modality for the purpose of object pattern recognition. Emphases are also put on data quality, qualitative validation of expected atmospheric spectral features, and qualitative comparison against another dataset of the same site using a different LWIR hyperspectral sensor. SPICE is a collaborative effort between the Army Research Laboratory, U.S. Army Armament RDEC, and more recently the Air Force Institute of Technology. It focuses on the collection and exploitation of longwave and midwave infrared (LWIR and MWIR) hyperspectral and polarimetric imagery. We concluded from this work that the quality of SPICE hyperspectral LWIR data is categorically comparable to other datasets recorded by a different sensor of similar specs; and adequate for algorithm research, given the scope of SPICE. The scope was to conduct a long-term infrared data collection of the same site with targets, using both sensing modalities, under various weather and non-ideal conditions. Then use the vast dataset and associated ground truth information to assess performance of the state of the art algorithms, while determining performance degradation sources. The expectation is that results from these assessments will spur new algorithmic ideas with the potential to augment pattern recognition performance in remote sensing applications. Over time, we are confident the SPICE database will prove to be an asset to the wide open remote sensing community.
The proposed paper recommends a new anomaly detection algorithm for polarimetric remote sensing applications based on the M-Box covariance test by taking advantage of key features found in a multi-polarimetric data cube. The paper demonstrates: 1) that independent polarization measurements contain information suitable for manmade object discrimination from natural clutter; 2) analysis between the variability exhibited by manmade objects relative to natural clutter; 3) comparison between the proposed M-Box covariance test with Stokes parameters S0 and S1, DoLP, RX Stokes, and PCA RX-Stokes; and finally 4) the data used for the comparison spans a full24-hour measurement.
This paper describes the end-to-end processing of image Fourier transform spectrometry data taken of surrogate tank targets at Picatinny Arsenal in New Jersey with the long-wave hyper-spectral camera HyperCam from Telops. The first part of the paper discusses the processing from raw data to calibrated radiance and emissivity data. The second part discusses the application of a range-invariant anomaly detection approach to calibrated radiance, emissivity and brightness temperature data for different spatial resolutions and compares it to the Reed-Xiaoli detector.
In imaging polarimetry, special consideration must be given to ensure proper spatial registration
between frames. Edge artifacts caused by the differencing of unregistered frames has the
potential to create significant spurious polarization signatures. To achieve 1/10th pixel
registration or better, a software based registration approach is often required. The focus of this
paper is to present an efficient algorithm for real time sub-pixel registration in a division-of-time
IR polarimeter based on a rotating polarizer. This algorithm has been implemented in a
commercially available rotating polarizer LWIR imaging polarimeter offered by Polaris Sensor
Technologies. This paper presents measurements of image nutation in a rotating polarizer LWIR
imaging polarimeter and real-time registration of image data from that same polarimeter. The
registration algorithm is based on an optimal 2D convolution. Examples of registered images are
provided as well as estimates of residual misregistration artifacts.
It is understood that Long Wave Infrared (LWIR) polarimetric imagery has the potential for detecting man-made objects
in natural clutter backgrounds. Unlike Spectral and conventional broadband, polarimetric imagery takes advantage of
the polarized signals emitted by the smooth surfaces of man-made materials. Studying the effect of how meteorological
conditions affect polarization signals is imperative in order to understand where and how polarimetric technology can be
beneficial to the war fighter. In this paper we intend to demonstrate the effects of weather on the performance of Stokes
vector components, S0, S1, S2, and the Degree of Linear Polarization (DOLP) as detectors of man-made materials. Using
the Hyperspectral Polarimetric Image Collection Experiment (SPICE) data collection, we analyze approximately one
thousand images and correlate the performance of each of the detection metrics to individual meteorological
measurements.
The Spectral and Polarimetric Imagery Collection Experiment (SPICE) is an ongoing collaborative effort that
commenced in February 2010 between the US Army ARDEC and Army Research Laboratory (ARL). SPICE is focused
on the collection of mid-wave and long-wave infrared imagery using hyperspectral, polarimetric, and broadband sensors.
The overall objective of SPICE is to collect a comprehensive database of the different modalities spanning multiple years
to capture sensor performance encompassing a wide variety of meteorological (MET) conditions, diurnal, and seasonal
changes inherent to Picatinny's northern New Jersey location.
Utilizing the Precision Armament Laboratory (PAL) tower at Picatinny Arsenal, the sensors are autonomously collecting
the desired data around the clock at multiple ranges containing surrogate 2S3 Self-Propelled Howitzer targets positioned
at different orientations in an open woodland field. This database allows for: 1) Understanding of signature variability
under adverse weather conditions; 2) Development of robust algorithms; 3) Development of new sensors; 4) Evaluation
of polarimetric technology; and 5) Evaluation of fusing the different sensor modalities.
In this paper, we will revisit the SPICE data collection objectives and the sensors deployed. We will present, in a
statistical sense, the integrity of the data in the long-wave infrared (LWIR) polarimetric database collected from
February through September 2010 and issues and lessons learned associated with a fully autonomous, around the clock
data collection. We will also demonstrate sample LWIR polarimetric imagery and the performance of the Stokes
parameters under adverse weather conditions.
The Spectral and Polarimetric Imagery Collection Experiment (SPICE) is a collaborative effort between the US Army
ARDEC and ARL that is focused on the collection of mid-wave and long-wave infrared imagery using hyperspectral,
polarimetric, and broadband sensors.
The objective of the program is to collect a comprehensive database of the different modalities over the course of 1 to 2
years to capture sensor performance over a wide variety of weather conditions, diurnal, and seasonal changes inherent to
Picatinny's northern New Jersey location.
Using the Precision Armament Laboratory (PAL) tower at Picatinny Arsenal, the sensors will autonomously collect the
desired data around the clock at different ranges where surrogate 2S3 Self-Propelled Howitzer targets are positioned at
different viewing perspectives in an open field. The database will allow for: 1) Understanding of signature variability
under adverse weather conditions; 2) Development of robust algorithms; 3) Development of new sensors; 4) Evaluation
of polarimetric technology; and 5) Evaluation of fusing the different sensor modalities.
In this paper, we will present the SPICE data collection objectives, the ongoing effort, the sensors that are currently
deployed, and how this work will assist researches on the development and evaluation of sensors, algorithms, and fusion
applications.
The Spectral and Polarimetric Imagery Collection Experiment (SPICE) is a collaborative effort between the US Army
ARDEC and ARL for the collection of mid-wave and long-wave infrared imagery using hyperspectral, polarimetric, and
broadband sensors.
The objective of the program is to collect a comprehensive database of the different modalities over the course of 1 to 2
years to capture sensor performance over a wide variety of adverse weather conditions, diurnal, and seasonal changes
inherent to Picatinny's northern New Jersey location.
Using the Precision Armament Laboratory (PAL) tower at Picatinny Arsenal, the sensors will autonomously collect the
desired data around the clock at different ranges where surrogate 2S3 Self-Propelled Howitzer targets are positioned at
different viewing perspectives at 549 and 1280m from the sensor location. The collected database will allow for: 1)
Understand of signature variability under the different weather conditions; 2) Development of robust algorithms; 3)
Development of new sensors; 4) Evaluation of hyperspectral and polarimetric technologies; and 5) Evaluation of fusing
the different sensor modalities.
In this paper, we will present the SPICE data collection objectives, the ongoing effort, the sensors that are currently
deployed, and how this work will assist researches on the development and evaluation of sensors, algorithms, and fusion
applications.
The midwave and longwave infrared regions of the electromagnetic spectrum contain rich information which can be
captured by hyperspectral sensors thus enabling enhanced detection of targets of interest. A continuous hyperspectral
imaging measurement capability operated 24/7 over varying seasons and weather conditions permits the evaluation of
hyperspectral imaging for detection of different types of targets in real world environments. Such a measurement site
was built at Picatinny Arsenal under the Spectral and Polarimetric Imagery Collection Experiment (SPICE), where two
Hyper-Cam hyperspectral imagers are installed at the Precision Armament Laboratory (PAL) and are operated
autonomously since Fall of 2009. The Hyper-Cam are currently collecting a complete hyperspectral database that
contains the MWIR and LWIR hyperspectral measurements of several targets under day, night, sunny, cloudy, foggy,
rainy and snowy conditions.
The Telops Hyper-Cam sensor is an imaging spectrometer that enables the spatial and spectral analysis capabilities using
a single sensor. It is based on the Fourier-transform technology yielding high spectral resolution and enabling high
accuracy radiometric calibration. It provides datacubes of up to 320x256 pixels at spectral resolutions of up to 0.25 cm-1.
The MWIR version covers the 3 to 5 μm spectral range and the LWIR version covers the 8 to 12 μm spectral range.
This paper describes the automated operation of the two Hyper-Cam sensors being used in the SPICE data collection.
The Reveal Automation Control Software (RACS) developed collaboratively between Telops, ARDEC, and ARL
enables flexible operating parameters and autonomous calibration. Under the RACS software, the Hyper-Cam sensors
can autonomously calibrate itself using their internal blackbody targets, and the calibration events are initiated by user
defined time intervals and on internal beamsplitter temperature monitoring. The RACS software is the first software
developed for COTS hyperspectal sensors that allows for full autonomous data collection capability for the user. The
accuracy of the automatic calibration was characterized and is presented in this paper.
Hyperspectral technology is currently being used by the military to detect regions of interest where potential targets may
be located. Weather variability, however, may affect the ability for an algorithm to discriminate possible targets from
background clutter. Nonetheless, different background characterization approaches may facilitate the ability for an
algorithm to discriminate potential targets over a variety of weather conditions. In a previous paper, we introduced a new
autonomous target size invariant background characterization process, the Autonomous Background Characterization
(ABC) or also known as the Parallel Random Sampling (PRS) method, features a random sampling stage, a parallel
process to mitigate the inclusion by chance of target samples into clutter background classes during random sampling;
and a fusion of results at the end. In this paper, we will demonstrate how different background characterization
approaches are able to improve performance of algorithms over a variety of challenging weather conditions. By using the
Mahalanobis distance as the standard algorithm for this study, we compare the performance of different characterization
methods such as: the global information, 2 stage global information, and our proposed method, ABC, using data that was
collected under a variety of adverse weather conditions. For this study, we used ARDEC's Hyperspectral VNIR Adverse
Weather data collection comprised of heavy, light, and transitional fog, light and heavy rain, and low light conditions.
We present a proof-of-principle demonstration using Sony's IBM Cell processor-based PlayStation 3 (PS3) to run-in
near real-time-a hyperspectral anomaly detection algorithm (HADA) on real hyperspectral (HS) long-wave infrared
imagery. The PS3 console proved to be ideal for doing precisely the kind of heavy computational lifting HS based
algorithms require, and the fact that it is a relatively open platform makes programming scientific applications feasible.
The PS3 HADA is a unique parallel-random sampling based anomaly detection approach that does not require prior
spectra of the clutter background. The PS3 HADA is designed to handle known underlying difficulties (e.g., target
shape/scale uncertainties) often ignored in the development of autonomous anomaly detection algorithms. The effort is
part of an ongoing cooperative contribution between the Army Research Laboratory and the Army's Armament,
Research, Development and Engineering Center, which aims at demonstrating performance of innovative algorithmic
approaches for applications requiring autonomous anomaly detection using passive sensors.
Hyperspectral ground to ground viewing perspective presents major challenges for autonomous window based detection.
One of these challenges has to do with object scales uncertainty that occur when using a window-based detection
approach. In a previous paper, we introduced a fully autonomous parallel approach to address the scale uncertainty
problem. The proposed approach featured a compact test statistic for anomaly detection, which is based on a principle of
indirect comparison; a random sampling stage, which does not require secondary information (range or size) about the
targets; a parallel process to mitigate the inclusion by chance of target samples into clutter background classes during
random sampling; and a fusion of results at the end. In this paper, we demonstrate the effectiveness and robustness of
this approach on different scenarios using hyperspectral imagery, where for most of these scenarios, the parameter
settings were fixed. We also investigated the performance of this suite over different times of the day, where the spectral
signatures of materials varied with relation to diurnal changes during the course of the day. Both visible to near infrared
and longwave imagery are used in this study.
Ground to ground, sensor to object viewing perspective presents a major challenge for autonomous window based object
detection, since object scales at this viewing perspective cannot be approximated. In this paper, we present a fully
autonomous parallel approach to address this challenge. Using hyperspectral (HS) imagery as input, the approach
features a random sampling stage, which does not require secondary information (range) about the targets; a parallel
process is introduced to mitigate the inclusion by chance of target samples into clutter background classes during random
sampling; and a fusion of results. The probability of sampling targets by chance within the parallel processes is modeled
by the binomial distribution family, which can assist on tradeoff decisions. Since this approach relies on the effectiveness
of its core algorithmic detection technique, we also propose a compact test statistic for anomaly detection, which is based
on a principle of indirect comparison. This detection technique has shown to preserve meaningful detections (genuine
anomalies in the scene) while significantly reducing the number of false positives (e.g. transitions of background
regions). To capture the influence of parametric changes using both the binomial distribution family and actual HS imagery, we conducted a series of rigid statistical experiments and present the results in this paper.
We present a multistage anomaly detection algorithm suite and suggest its application to chemical plume detection
using hyperspectral (HS) imagery. This approach is proposed to handle underlying difficulties (e.g., plume shape/scale
uncertainties) facing the development of autonomous anomaly detection algorithms. The approach features four stages:
(i) scene random sampling, which does not require secondary information (shape and scale) about potential effluent
plumes; (ii) anomaly detection; (iii) parallel processes, which are introduced to mitigate the inclusion by chance of
potential plume samples into clutter background classes; and (iv) fusion of results. The probabilities of taking plume
samples by chance within the parallel processes are modeled by the binomial distribution family, which can be used to
assist on tradeoff decisions. Since this approach relies on the effectiveness of its core anomaly detection technique, we
present a compact test statistic for anomaly detection, which is based on an asymmetric hypothesis test. This anomaly
detection technique has shown to preserve meaningful detections (genuine anomalies in the scene) while significantly
reducing the number of meaningless detections (transitions of background regions). Results of a proof of principle
experiment are presented using this approach to test real HS background imagery with synthetically embedded gas
plumes. Initial results are encouraging.
Future sensing technologies are needed to provide higher accuracy, lower power consumption and occupy small real estate within munitions. The novel ideas being supported at the Army Research Development Engineering Center (ARDEC) at Dover, New Jersey, uses principles of electromagnetic propagation and the properties of waveguide cavities with various geometries to develop a new class of sensors for onboard direct measurement of the angular orientation and position of objects in flight and applications such as mobile robotic platforms. Currently available sensors for munitions are based on inertia, optics or heat. Inertia based sensing generally suffers from drift, noise and the currently available sensors cannot survive high firing accelerations while maintaining the required measurement sensitivity. Optical technologies generally have short range and require line-of-site. The sensing technologies presented in this paper employ radio frequency, make direct measurement of position and orientation, and do not require added information for their operation. The presented sensors employ waveguide cavities that are embedded into the structure of munitions. It is shown that the geometry of the waveguide cavity can be designed to achieve high angular orientation sensitivity with respect to a reference, polarized electromagnetic field. In this paper, the theoretical fundamentals describing the operation of the developed sensors are described. Studies of the interaction of the polarized signals with various waveguides and cavity geometries are presented. Simulations results as well as experimental results validating the theoretical and the simulation results are provided. The simulation and experimental results clearly demonstrate the potentials of the developed position and angular orientation sensors in general, and to munitions in particular.
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