An unmanned aerial vehicle (UAV) is exploited to characterize in situ the high-band antennas (HBAs) of the low-frequency array (LOFAR) CS302 station located in Exloo, The Netherlands. The size of an HBA array is about 30 m. The Fraunhofer distance (a few kilometers) is not reachable in the frequency band (120 to 240 MHz) within the flight regulation limits. Therefore, far-field patterns cannot be directly measured. The UAV, equipped with an radio frequency synthesizer and a dipole antenna, flies in the near-field region of the considered array. Measurement of three different frequencies (124, 150, and 180 MHz) is efficiently made during the same UAV flight. The near-field focusing method is exploited to validate the far-field pattern of the array under test within an angular range around the beam axis. Such a technique avoids both the time consuming λ / 2 sampling of the aperture field and the further application of computationally heavy near-field to far-field transformations. The array beam is well reconstructed in the main lobe and first sidelobes within a 2D scan plane sampled with a radial raster. A further postprocessing technique is proposed and validated on a subarray of HBAs. It suggests efficient ways for the future characterization of regular aperture arrays for SKA-MID Phase 2.
The physical and biological properties of Arctic ice and coastal benthos remain poorly understood due to the difficulty of accessing these substrates in ice-covered waters. A LiDAR system deployed on an autonomous underwater vehicle (AUV) can interrogate these 3D surfaces for physical and biological properties simultaneously. Using our understanding of the absorption, inelastic scattering (fluorescent), and elastic scattering properties of photosynthetic micro- and macroalgae excited by lasers, we present results of in situ tank tests using a two-wavelength (473 nm, 532 nm) prototype to evaluate both fluorosensor and differential absorption (DIAL) approaches using reflectance standards and selected macroalgae as targets.
Goal of this work is to present and validate an underwater object spectral recognition methodology for fluorescence LIDAR signals by using an underwater fluorescence LIDAR propagation model. The spectral recognition methodology is aimed at deciding if an underwater object detected in the water column can be identified as belonging to a data base of objects of interest characterized by known fluorescence spectral signatures. The methodology needs to compensate the received signal for the water column effects in order to derive an estimate of the underwater object fluorescence spectrum to be used for spectral recognition. By using an underwater fluorescence LIDAR propagation model developed ad hoc, the methodology may be validated in different system, geometric, and environmental conditions. Experimental results obtained in two different acquisition scenarios show that the underwater object recognition methodology is promising for recognizing objects submerged in the water column at different depths and highlight the utility of the developed LIDAR propagation model for assessing the object recognition performance that may be experienced in various different acquisition conditions.
KEYWORDS: Sensors, Signal to noise ratio, Hyperspectral imaging, Data modeling, Interference (communication), Detection and tracking algorithms, Target detection, Data analysis, Signal detection, Statistical analysis
Recent studies on global anomaly detection (AD) in hyperspectral images have focused on non-parametric approaches that seem particularly suitable to detect anomalies in complex backgrounds without the need of assuming any specific model for the background distribution. Among these, AD algorithms based on the kernel density estimator (KDE) benefit from the flexibility provided by KDE, which attempts to estimate the background probability density function (PDF) regardless of its specific form. The high computational burden associated with KDE requires KDE-based AD algorithms be preceded by a suitable dimensionality reduction (DR) procedure aimed at identifying the subspace where most of the useful signal lies. In most cases, this may lead to a degradation of the detection performance due to the leakage of some anomalous target components to the subspace orthogonal to the one identified by the DR procedure. This work presents a novel subspace-based AD strategy that combines the use of KDE with a simple parametric detector performed on the orthogonal complement of the signal subspace, in order to benefit of the non-parametric nature of KDE and, at the same time, avoid the performance loss that may occur due to the DR procedure. Experimental results indicate that the proposed AD strategy is promising and deserves further investigation.
Fluorescence LIght Detection And Ranging (LIDAR) systems have been proven powerful for detecting and recognizing underwater objects in several applications. Such Fluorescence systems have been employed mainly for detecting and recognizing oil spill and chemicals dissolved in the sea and to identify phytoplankton species. This work focuses on the use of Fluorescence LIDAR systems in underwater object recognition applications. In fact, the fluorescence spectra induced over object and materials may be exploited to derive chemical-physical information about object nature useful to recognition. Specifically, a model for fluorescence LIDAR transmission in the water medium, both in the presence and absence, of an underwater object is proposed. The developed model describes the interaction of the transmitted laser beam with underwater objects, bottom, and water molecules. Specifically, the fluorescence return signals are modeled involving the inelastic backscattering contributions due to the Raman scattering by water molecules and fluorescence by water constituents, bottom, and objects. A range of simulations have been performed modeling the immersion of an object at different depths within the water column for a variety of system characteristics and water environmental conditions. Simulation results show the model flexibility for reproducing the signals acquired in different operational scenarios on the basis of various system parameters, acquisition geometries, and water environments. The transmission model may be useful to predict the performance of a given fluorescence LIDAR in specific underwater object detection and recognition applications.
Seven countries within the European Defence Agency (EDA) framework are joining effort in a four year project (2009-2013) on Detection in Urban scenario using Combined Airborne imaging Sensors (DUCAS). Data has been collected in a joint field trial including instrumentation for 3D mapping, hyperspectral and high resolution imagery together with in situ instrumentation for target, background and atmospheric characterization. Extensive analysis with respect to detection and classification has been performed. Progress in performance has been shown using combinations of hyperspectral and high spatial resolution sensors.
In the past few years, spectral analysis of data collected by hyperspectral sensors aimed at automatic anomaly detection
has become an interesting area of research. In this paper, we are interested in an Anomaly Detection (AD) scheme for
hyperspectral images in which spectral anomalies are defined with respect to a statistical model of the background Probability
Density Function (PDF).The characterization of the PDF of hyperspectral imagery is not trivial. We approach the
background PDF estimation through the Parzen Windowing PDF estimator (PW). PW is a flexible and valuable tool for
accurately modeling unknown PDFs in a non-parametric fashion. Although such an approach is well known and has been
widely employed, its use within an AD scheme has been not investigated yet. For practical purposes, the PW ability to
estimate PDFs is strongly influenced by the choice of the bandwidth matrix, which controls the degree of smoothing of
the resulting PDF approximation. Here, a Bayesian approach is employed to carry out the bandwidth selection. The resulting
estimated background PDF is then used to detect spectral anomalies within a detection scheme based on the
Neyman-Pearson approach. Real hyperspectral imagery is used for an experimental evaluation of the proposed strategy.
This work was motivated by the availability of a new ground truthed hyperspectral data set, freely accessible to the
scientific community for target detection algorithm testing. In our research, we are interested in physics-based
approaches to target detection, i.e. those techniques aimed at modeling the radiation transfer within the atmosphere in
order to account for atmospheric/viewing/illumination effects. This is a crucial aspect in target detection applications,
where the available information resides in the sensor-acquired radiance image and field-measured spectral reflectances of
the targets. Properly backing out the aforementioned effects allows detection to be performed in either of the two
domains, i.e. radiance or reflectance. As part of our research into the use of physics-based radiative transfer modeling
(RTM) for target detection with these new data, it was important to accurately analyze the available a priori information
concerning data acquisition, and investigate the value of enhancing this information by making use of freely accessible
meteorological and environmental data. In this work, the characterization procedure of the RTM parameters applied to
these data is described, and the corresponding RTM parameters thus obtained are reported. A range of variation for some
of these parameters were determined as well, in order to allow for a certain degree of variability around nominal
conditions (e.g. spatial variability within the scene, non-perfect acquisition condition knowledge, etc.). Target detection
results obtained by adopting the RTM parameters attained by the characterization procedure show similar performance in
both the radiance and the reflectance domains.
Anomaly detection in hyperspectral images has proven valuable in many applications, such as hazardous material and mine detection. The benchmark anomaly detector is the Reed-Xiaoli (RX) detector, which is based on the local multivariate normality of background. The RX algorithm, along with its many modified versions, has been widely explored, and the main concerns identified are related to local background covariance matrix estimation. The small sample size, local background nonhomogeneity, and the presence of target pixels within the estimation window are factors that can deeply affect local background covariance matrix estimation. These critical aspects may occur together in the same operational scenario, and they may strongly impair the detection performance. However, due to their intrinsic difference, these aspects have been typically discussed within different frameworks, disregarding the possible existing connections while developing different approaches to solution. We investigate these critical aspects, along with their impact on the detection process, from an operational detection perspective. The approaches to solution are critically analyzed, discussing possible links and connections. Real hyperspectral data are employed for assessing if the algorithms, designed ad hoc to solve a specific problem, can either handle more complex situations, or bring about further complications.
In this work, Spectral Signature-Based Target Detection (SSBTD) as applied to airborne monitoring for surveillance and
reconnaissance of ground targets is addressed, and techniques that can help to approach in-flight processing are analyzed
from this perspective. In fact, SSBTD is a challenging task from an operating viewpoint, mainly due to the crucial
atmospheric compensation step, which is required to make the target measured reflectance comparable to the sensoracquired
radiance. Both physics-based radiative transfer modeling techniques and empirical scene-based methods are
considered for atmospheric compensation, and their applicability and adaptability to in-flight processing are discussed.
Experimental data acquired by a hyperspectral sensor operating in the Visible Near-InfraRed range are employed for
analysis. The data consist in multiple images collected during subsequent flights performed over the same scene. Such a
situation well reproduces the typical scenario of regularly monitoring an area of interest, and can, therefore, be adopted
for examining the aforementioned approaches from an in-flight applicability perspective. Target detection results are
analyzed and discussed by examining objective performance measures such as the Receiver Operating Characteristic
(ROC) curves.
The benchmark anomaly detection algorithm for hyperspectral images is the Reed-Xiaoli (RX) Detector, which is based
on the Local Multivariate Normality of background. RX algorithm, along with its many modified versions, has been
widely explored, and the main concerns identified are related to local background covariance matrix estimation. Besides
the well-known small-sample size problem, other limitations have been found affecting covariance matrix estimation,
e.g. local background non-homogeneity and contamination from adjacent targets. These critical aspects are deeply
different in nature, like the situations from which they arise, and hence they have been typically discussed within
different frameworks, disregarding possible existing links while developing different approaches to solution.
Nevertheless, these critical aspects may occur together in reality, and all of them have to be taken into consideration
when approaching anomaly detection, since they may strongly affect detection performance. Therefore, an analysis of
the possible existing connections seems crucial in order to asses if existing algorithms, maybe designed ad-hoc to solve a
specific problem, can handle more complex situations. In this work, the aforementioned limitations have been
investigated from an anomaly detection perspective, and the corresponding approaches to improved covariance matrix
estimation have been analyzed by using real hyperspectral data.
Tracking of vehicles, in both radiance and reflectance hyperspectral imagery, was analyzed using traditional
reflectance domain and forward predicting physics based target detection algorithms. The investigation consisted
of locating vehicles in one image followed by locating the same (relocated) vehicles in another image that was
collected at a different time. Traditional approaches to hyperspectral target detection involve the application
of detection algorithms to atmospherically compensated imagery. Rather than compensate the imagery, a more
recent approach uses physical models to generate radiance signature spaces. The signature space is actually
a representation of what the target might look like to the sensor as the reflectance propagates through the
atmosphere. The model takes into account atmospherics, illumination conditions and target reflectivity. Different
collection times have varying atmospheric conditions leading to changes in the sensor-reaching radiance spectrum.
A forward predicting physical model, applied in the radiance domain, handles this variation. The data used in this
study is a new ground truthed hyperspectral data set, collect with the airborne HyMap sensor, and which is now
freely available to the community through the web for evaluation, testing, and algorithm development. Vehicles
were relocated across various flight lines. This paper compares vehicle detection results, across these multiple
images collected within a short time period, using both the physical model and the traditional atmospheric
compensation approach.
This work presents a comparative experimental analysis of different Anomaly Detectors (ADs) carried out on a high
spatial resolution data set acquired by the prototype hyperspectral sensor SIM-GA. The benchmark AD for hyperspectral
anomaly detection is the Reed-Xiaoli (RX) algorithm. Its main limitation is the assumption that the local background can
be modeled by a Gaussian distribution. In the literature, several ADs have been presented, most of them trying to cope
with the problem of non-Gaussian background. Despite the variety of works carried out on such algorithms, it is difficult
to find a comparative analysis of these methodologies performed on the same data set and therefore in identical operating
conditions. In this work, the most known ADs, such as the RX, Orthogonal Subspace Projection (OSP) based algorithms,
the Cluster Based AD (CBAD), and the Signal Subspace Processing AD (SSPAD) are analyzed and compared,
highlighting their most interesting characteristics. The performance is evaluated on a new data set relative to a rural
scenario, in which several man-made targets have been embedded. The non-homogeneous nature of the background,
enhanced by the high spatial resolution of the sensor, and the presence of man-made artifacts, like buildings and
vehicles, make the anomaly detection process very challenging. Performance comparison is carried out on the basis of a
joint analysis of the Receiving Operative Characteristics and the image statistics. For this data set, the best performance
is obtained by the strong background suppression ability of the OSP-based algorithm.
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