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.
We report the results of a diurnal study in which radiometrically calibrated polarimetric and conventional thermal
imagery are recorded in the MidIR and LWIR to identify and compare the respective time periods in which minimum
target contrast is achieved. The MidIR polarimetric sensor is based on a division-of-aperture approach and has a
640x512 InSb focal-plane array, while the LWIR polarimetric sensor uses a spinning achromatic retarder to perform the
polarimetric filtering and has a 324x256 microbolometer focal-plane array. The images used in this study include the S0
and S1 Stokes images of a scene containing a military vehicle and the natural background. In addition, relevant
meteorological parameters measured during the test period include air temperature, ambient loading in the LWIR,
relative humidity, cloud cover, height, and density. The data shows that the chief factors affecting polarimetric contrast
in both wavebands are the amount of thermal emission from the objects in the scene and the abundance of MidIR and
LWIR sources in the optical background. In particular, it has been observed that the MidIR polarimetric contrast was
positively correlated to the presence of MidIR sources in the optical background, while the LWIR polarimetric contrast
was negatively correlated to the presence of LWIR sources in the optical background.
In recent years there has been an increased interest in using polarimetric imaging sensors for terrestrial remote sensing
applications because of their ability to discriminate manmade objects in a natural clutter background. However, adverse
weather limits the performance of these sensors. Long Wave Infrared (LWIR) polarimetric sensor data of a scene
containing manmade objects in a natural clutter background is compared with simultaneously collected environmental
data. In this paper, a metric is constructed from the Stokes parameter S1 and is correlated with some environmental
channels. There are differences in the correlation outputs, with the sensor data metric positively correlated with some
environmental channels, negatively correlated with some channels and uncorrelated with other channels. Results from
real data measurements are presented and interpreted. An uncooled LWIR sensor using an achromatic retarder to capture
the polarimetric states performed the data collection. The environmental channels include various meteorological
channels, radiation loading and soil properties.
We report the results of a multi-day diurnal study in which radiometrically calibrated polarimetric and conventional
thermal imagery is recorded in the LWIR to identify/compare the respective time periods in which minimum target
contrast is achieved, e.g., thermal inversion periods are typically experienced during dusk and dawn. Imagery is recorded
with a polarimetric IR sensor employing a 324x256 microbolometer array using a spinning achromatic retarder to
perform the polarimetric filtering. The images used in this study include the S0, normalized S1, and normalized S2 Stokes
images and the degree of linear polarization (DOLP) images of a scene containing military vehicles and the natural
background. In addition, relevant meteorological parameters measured during the test period include air temperature,
ambient loading in the LWIR, relative humidity, and cloud cover, height and density. The data shows that the chief
factors affecting polarimetric contrast are the amount of thermal emission from the objects in the scene and the
abundance of LWIR sources in the optical background. In addition, we found that contrast between targets and
background within polarimetric images often remains relatively high during periods of low thermal contrast.
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.
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.
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