Physical Sciences Inc. has developed an ultra-compact shortwave infrared (SWIR) staring mode hyperspectral imaging (HSI) sensor with an additional visible full motion video (FMV) capability. The innovative HSI design implements a programmable micro-electromechanical system entrance slit that breaks the interdependence between vehicle speed, frame rate, and spatial resolution of conventional push-broom systems and enables staring-mode operation without cooperative motion of the host vehicle or aircraft. The FMV and HSI components fit within 1000 cm3, weigh a total of 2.1 lbs., and draw 15 W of power. The sensor mechanical design is compatible with gimbal-based deployment allowing for easy integration into ground vehicles or aircrafts. The FMV is capable of achieving NIRS-6 imagery over a 6°×6° field-of-view (FOV) at a 1500 ft. standoff. The SWIR HSI covers a spectral range of 900-1605 nm with a 15 nm spectral resolution, and interrogates a 5°×5° FOV per 1.6 s with a 2.18 mrad instantaneous FOV (1 m ground sample distance at 1500 ft.). A series of outdoor tests at standoffs up to 300 ft. have been conducted that demonstrate the payload’s capability to acquire HSI information. The payload has direct utility towards diverse remote sensing applications such as vegetation monitoring, geological mapping, surveillance, etc. The data product utility is demonstrated through the spectral identification of materials (e.g. foam and cloth) placed in the sensor’s FOV.
Numerous methods exist to perform hyperspectral target detection. Application of these algorithms often requires the data to be atmospherically corrected. Detection for longwave infrared data typically requires surface temperature estimates as well. This work compares the relative robustness of various target detection algorithms with respect to atmospheric compensation and target temperature uncertainty. Specifically, the adaptive coherence estimator and spectral matched filter will be compared with subspace detectors for various methods of atmospheric compensation and temperature-emissivity separation. Comparison is performed using both daytime and nighttime longwave infrared hyperspectral data collected at various altitudes for various target materials.
KEYWORDS: Target detection, Sensors, Calibration, Detection and tracking algorithms, Latex, Image sensors, Staring arrays, Cameras, Signal to noise ratio, Hyperspectral imaging, Scene based nonuniformity corrections
Hyperspectral imaging sensors suffer from pixel-to-pixel response nonuniformity that manifests as fixed pattern noise (FPN) in collected data. FPN is typically removed by application of flat-field calibration procedures and nonuniformity correction algorithms. Despite application of these techniques, some amount of residual fixed pattern noise (RFPN) may persist in the data, negatively impacting target detection performance. In this paper we examine the conditions under which RFPN can impact detection performance using data collected in the SWIR across a range of target materials. We examine the application of scene-based nonuniformity correction (SBNUC) algorithms and assess their ability to remove RFPN. Moreover, we examine the effect of RFPN after application of these techniques to assess detection performance on a number of target materials that range in inherent separability from the background.
In this paper, we consider change detection in the longwave infrared (LWIR) domain. Because thermal emission is the dominant radiation source in this domain, differences in temperature may appear as material changes and introduce false alarms in change imagery. Existing methods, such as temperature-emissivity separation and alpha residuals, attempt to extract temperature-independent LWIR spectral information. However, both methods remain susceptible to residual temperature effects which degrade change detection performance. Here, we develop temperature-robust versions of these algorithms that project the spectra into approximately temperatureinvariant subspaces. The complete error covariance matrix for each method is also derived so that Mahalanobis distance may be used to quantify spectral differences in the temperature-invariant domain. Examples using synthetic and measured data demonstrate substantial performance improvement relative to the baseline algorithms.
Hyperspectral imaging systems are typically characterized in a laboratory environment to determine band centers and bandwidths associated with the collected data. This procedure is commonly referred to as spectral calibration. Generally, this wavelength information is assumed to be accurate and spatially-invariant. Exploitation of hyperspectral data utilizes this information for atmospheric compensation and/or resampling of library data for use in detection and identification applications. The spectral information can be inaccurate due to system aberrations, such as spectral smile, or due to spectral calibration error. This work examines the impact of spectral smile on hyperspectral exploitation.
Hyperspectral images often contain hundreds of spectral bands. Man-made and natural materials usually exhibit variability in their reflective and emissive response across these bands, which is exploitable via target detection algorithms. The high-dimensional nature of hyperspectral data has driven studies that explored ways to reduce spectral dimensionality without adversely affecting spectral target detection. Recently, spatial-spectral feature extraction techniques have demonstrated additional discrimination capability versus spectral-only approaches in VNIR, SWIR, and LWIR hyperspectral imagery. When spatial descriptors are applied to spectral bands within a hyperspectral image, the length of a resulting spatial-spectral feature vector can be several times that of the original spectrum. While numerous efforts to reduce the dimensionality of hyperspectral imagery have been undertaken, they have not been commonly extended to the spatial-spectral domain. In this work, we address the relatively new problem of spatial-spectral dimensionality reduction through a strategy designed to remove features that neither negatively affect a target detection algorithm's capability to detect targets nor detract from that algorithm's ability to discriminate between targets in an exemplar signature library.
Hyperspectral imagers are often used in an airborne platform and have shown utility in material detection and identification. In these scenarios the sensor is flown over a scene and the motion of the aircraft and/or the scanning action of a gimbal causes motion blur in the collected imagery. The only solution to this problem is to use expensive pointing gimbals to stabilize the imager and allow for step scanning. The effects of motion blur are dependent on scan speed and integration time of the imager. A Headwall visible hyperspectral imager collected data of a knife edge target to experimentally show the effect of motion blur on spatial resolution in the along scan direction. A data cube was then used to simulate single-pixel and sub-pixel targets. A simulated step scanned scene and continuously scanned scene were then compared using target detection results. The continuous scanned scene showed a twenty five percent drop in performance over the step scanned scene.
In this paper, we develop and evaluate change detection algorithms for longwave infrared (LWIR) hyperspectral imagery. Because measured radiance in the LWIR domain depends on unknown surface temperature, care must be taken to prevent false alarms resulting from in-scene temperature differences that appear as material changes. We consider two strategies to mitigate this effect. In the first, pre-processing via traditional temperature-emissivity separation (TES) yields approximately temperature-invariant emissivity vectors for use in change detection. In the second, we adopt a minimax approach that minimizes the maximal spectral deviation between measurements. While more computationally demanding, the second approach eliminates spectral density assumptions in traditional TES and provides superior change detection performance. Examples on synthetic and measured data quantify computational complexity and detection performance.
Hyperspectral target detection typically relies upon libraries of material reflectance and emissivity signatures. Application to real-world, airborne data requires estimation of atmospheric properties in order to convert reflectance/emissivity signatures to the sensor data domain. In the longwave infrared, an additional nuisance parameter of surface temperature exists that further complicates the signature conversion process. A significant amount of work has been done in atmospheric compensation and temperature-emissivity-separation techniques. This work examines the sensitivity of target detection performance for various materials with respect to target signature mismatch introduced from atmospheric compensation error or target temperature mismatch. Additionally, the impact of dimensionality reduction via principal components analysis is assessed.
Spatial-spectral feature extraction algorithms – such as those based on spatial descriptors applied to selected spectral bands within a hyperspectral image – can provide additional discrimination capability beyond traditional spectral-only approaches. However, when attempting to detect a target with such algorithms, an exemplar target signature is often manually derived from the hyperspectral images representation in the spatial-spectral feature space. This requires a reference image in which the targets location is known. Additionally, the scenebased signature captures only the representation of the target under certain collection conditions from a specific sensor, namely, illumination level and atmospheric composition, look angle, and target pose against a specific background. A detection algorithm utilizing this spatial-spectral signature (or the spatial descriptor itself) that is sensitive to changes in these collection conditions could suffer a loss in performance should the new conditions significantly deviate from the exemplars case. To begin to overcome these limitations, we formulate and evaluate the effectiveness of a modeling technique for synthesizing exemplar spatial-spectral signatures for solid targets, particularly when the spatial structure of the target of interest varies due to pose or obscuration by the background, and when applicable, the target temperature varies. We assess the impact of these changes on a group of spatial descriptors responses to guide the modeling process for a set of two-dimensional targets specifically designed for this study. The sources of variability that most affect each descriptor are captured in target subspaces, which then form the basis of new spatial-spectral target detection algorithms.
Airborne hyperspectral imaging (HSI)has shown utility in material detection and identification. Recent interest in longwave infrared (LWIR) HSI systems operating in the 7-14 micron range has developed due to strong spectral features of minerals, chemicals, and gaseous effluents. LWIR HSI has the advantage over other spectral bands by operating in day or night scenarios because emitted/reflected thermal radiation rather than reflected sunlight is measured. This research seeks to determine the most effective methods to perform model-based atmospheric compensation (AC) of LWIR HSI data using two existing atmospheric radiative transfer (RT) models, MODTRAN and LEEDR. MODTRAN is the more established RT model, but it lacks LEEDRs robust capability to generate realistic atmospheric profiles from probabilistic climatology or observations and forecasts from numerical weather prediction (NWP) models. The advantage of LEEDR’s ability to generate atmospheres is tested by using LEEDR atmospheres, a MODTRAN standard model, and radiosonde data to perform AC on an airborne hyperspectral datacube with nadir looking geometry. This work investigates the potential benefit of LEEDR’s weather/climatology tools for improving and/or expediting the AC process for LWIR HSI.
KEYWORDS: Long wavelength infrared, Sensors, Spectroscopy, Calibration, Black bodies, Signal to noise ratio, Staring arrays, Prisms, Cameras, Data processing
Preliminary testing of a three-slit prism-based spectrometer is presented to test means of exploiting data from a multi-slit spectrometer as well as some potential ways of dealing with complications that arise when using multiple slits. When using a multiple slit spectrometer to boost SNR there are two primary concerns: first, the spectral axis of each slit must be nearly identical to effective average and second, the image from each slit must be well-registered. Based on some of these complications it seems, given the current technology, the best operational mode is to use the sensor to increase area coverage.
Hyperspectral sensors operating in the long wave infrared (LWIR) have a wealth of applications including remote material identification and rare target detection. While statistical models for modeling surface reflectance in visible and near-infrared regimes have been well studied, models for the temperature and emissivity in the LWIR have not been rigorously investigated. In this paper, we investigate modeling hyperspectral LWIR data using a statistical mixture model for the emissivity and surface temperature. Statistical models for the surface parameters can be used to simulate surface radiances and at-sensor radiance which drives the variability of measured radiance and ultimately the performance of signal processing algorithms. Thus, having models that adequately capture data variation is extremely important for studying performance trades. The purpose of this paper is twofold. First, we study the validity of this model using real hyperspectral data, and compare the relative variability of hyperspectral data in the LWIR and visible and near-infrared (VNIR) regimes. Second, we illustrate how materials that are easily distinguished in the VNIR, may be difficult to separate when imaged in the LWIR.
KEYWORDS: Long wavelength infrared, Infrared sensors, Infrared radiation, Signal processing, Temperature metrology, Monte Carlo methods, Databases, Sensors, Error analysis, Data modeling
Signal processing for long-wave infrared (LWIR) sensing is made complicated by unknown surface temperatures in a scene which impact measured radiance through temperature-dependent black-body radiation of in-scene objects. The unknown radiation levels give rise to the temperature-emissivity separation (TES) problem describing the intrinsic ambiguity between an object’s temperature and emissivity. In this paper we present a novel Bayesian TES algorithm that produces a probabilistic posterior estimate of a material’s unknown temperature and emissivity. The statistical uncertainty characterization provided by the algorithm is important for subsequent signal processing tasks such as classification and sensor fusion. The algorithm is based on Markov chain Monte Carlo (MCMC) methods and exploits conditional linearity to achieve efficient block-wise Gibbs sampling for rapid inference. In contrast to existing work, the algorithm optimally incorporates prior knowledge about inscene materials via Bayesian priors which may optionally be learned using training data and a material database. Examples demonstrate up to an order of magnitude reduction in error compared to classical filter-based TES methods.
Hyperspectral imaging (HSI) combined with target detection and identification algorithms require spectral signatures for target materials of interest. The longwave infrared (LWIR) region of the electromagnetic spectrum is dominated by thermal emission, and thus, estimates of target temperature are necessary for emissivity retrieval through temperature-emissivity separation or for conversion of known emissivity signatures to radiance units. Therefore, lack of accurate target temperature information poses a significant challenge for target detection and identification algorithms. Previous studies have demonstrated both LWIR target detection using signature subspaces and visible/shortwave subpixel target identification. This work compares adaptive coherence estimator (ACE) and subspace target detection algorithms for various target materials, atmospheric compensation algorithms, and imagery domains (radiance or emissivity) for several data sets. Preliminary results suggest that target detection in the radiance and emissivity domains is complementary, in the sense that certain material classes may be more easily detected using subspaces, while others require conversion to emissivity space. Furthermore, a radiance domain LWIR material identification algorithm that accounts for target temperature uncertainty is presented. The latter algorithm is shown to effectively distinguish between materials with a high degree of spectral similarity.
Exploitation of longwave infrared hyperspectral imagery often requires atmospheric compensation in order to retrieve intrinsic material emissivity properties. For materials possessing subtle spectral features and those with lower emissivity, downwelling radiance plays an important role in the atmospheric compensation process. Most atmospheric compensation algorithms use an estimate of the total downwelling radiance integrated over the hemisphere. However, for tilted surfaces and non-nadir imaging scenarios, directional downwelling radiance information may be required. This work examines collection of directional downwelling radiance measurements using a Fourier transform infrared spectrometer. Specifically, work is done to determine a minimum number of sky angles to measure for which the remainder of the directional sky measurements can be estimated with minimal error through the use of simple data fitting models.
KEYWORDS: Data modeling, Reflectivity, Temperature metrology, High dynamic range imaging, Radiative transfer, Long wavelength infrared, Atmospheric modeling, Spectral models, Clouds, Thermal modeling
The sensitivity of hyper-spectral remote sensing to the directional reflectance of surfaces was studied using both laboratory and field measurements. Namely, the effects of the specular- and diffuse-reflectance properties of a set of eight samples, ranging from high to low in both total reflectance and specularity, on diffuse-only and diffusespecular radiative transfer models in the long-wave infrared (LWIR, 7-14-μm wavelength) were studied. The samples were measured in the field as a set of eight panels, each in two orientations, with surface normal pointing toward zenith and tipped at 45° from zenith. The field-collected data also included down-welling spectral sky radiance at several angles from zenith to the horizon, ground spectral radiance, panel spectral radiances in both orientations, Infragold® spectral radiances in both orientations near each panel location, and panel temperatures. Laboratory measurements included spectral hemispherical, specular and diffuse directional reflectance (HDR, SDR and DDR) for each sample for several reflectance angles with respect to the surface normal. The diffuse-only radiative transfer model used the HDR data, while the diffuse-specular model used the SDR and DDR data. Both calculated spectral reflected and self-emitted radiances for each panel, using the field-collected sky radiance data to avoid uncertainties associated with atmospheric models. The modeled spectral radiances were then compared to the field-collected values to quantify differences in moving from an HDR-based model to an SDR/DDR model in the LWIR for a variety of surface-reflectance types.
Presented is a new hyperspectral imager design based on multiple slit scanning. This represents an innovation in the classic trade-off between speed and resolution. This LWIR design has been able to produce data-cubes at 3 times the rate of conventional single slit scan devices. The instrument has a built-in radiometric and spectral calibrator.
The majority of hyperspectral target detection algorithms are developed from statistical data models employing stationary background statistics or white Gaussian noise models. Stationary background models are inaccurate as a result of two separate physical processes. First, varying background classes often exist in the imagery that possess different clutter statistics. Many algorithms can account for this variability through the use of subspaces or clustering techniques. The second physical process, which is often ignored, is a signal-dependent sensor noise term. For photon counting sensors that are often used in hyperspectral imaging systems, sensor noise increases as the measured signal level increases as a result of Poisson random processes. This work investigates the impact of this sensor noise on target detection performance. A linear noise model is developed describing sensor noise variance as a linear function of signal level. The linear noise model is then incorporated for detection of targets using data collected at Wright Patterson Air Force Base.
KEYWORDS: Bidirectional reflectance transmission function, High dynamic range imaging, Data modeling, Long wavelength infrared, Atmospheric modeling, Solar radiation models, Nickel, Remote sensing, Databases, Reflectivity
In the LWIR, often one assumes a scene does not contain solar reflection. To test this assumption, a simple scene model is analyzed for two BRDFs: Lambertian and measured data. For certain geometries, non-negligible solar reflection is observed at 8, 10, and 12 microns when using the BRDF data. Additionally, the wavelength variation of the pupil plane radiance data differs from the Lambertian case when using a BRDF, not just the magnitude. These results suggest that even in the LWIR, BRDFs should be incorporated to account for solar radiance effects.
Hyperspectral imaging systems are currently used for numerous activities related to spectral identification of
materials. These passive imaging systems rely on naturally reflected/emitted radiation as the source of the
signal. Thermal infrared systems measure radiation emitted from objects in the scene. As such, they can
operate at both day and night. However, visible through shortwave infrared systems measure solar illumination
reflected from objects. As a result, their use is limited to daytime applications. Omni Sciences has produced
high powered broadband shortwave infrared super-continuum laser illuminators. A 64-watt breadboard system
was recently packaged and tested at Wright-Patterson Air Force Base to gauge beam quality and to serve as a
proof-of-concept for potential use as an illuminator for a hyperspectral receiver. The laser illuminator was placed
in a tower and directed along a 1.4km slant path to various target materials with reflected radiation measured
with both a broadband camera and a hyperspectral imaging system to gauge performance.
KEYWORDS: Expectation maximization algorithms, Spectral coherence, Spatial coherence, Hyperspectral imaging, Signal to noise ratio, Spectral models, Amplifiers, Radio propagation, Binary data, Control systems
In hyperspectral unmixing, the objective is to decompose an electromagnetic spectral dataset measured over M spectral bands and T pixels, into N constituent material spectra (or “endmembers”) with corresponding spatial abundances. In this paper, we propose a novel approach to hyperspectral unmixing (i.e., joint estimation of endmembers and abundances) based on loopy belief propagation. In particular, we employ the bilinear generalized approximate message passing algorithm (BiG-AMP), a recently proposed belief-propagation-based approach to matrix factorization, in a “turbo” framework that enables the exploitation of spectral coherence in the endmembers, as well as spatial coherence in the abundances. In conjunction, we propose an expectation- maximization (EM) technique that can be used to automatically tune the prior statistics assumed by turbo BiG-AMP. Numerical experiments on synthetic and real-world data confirm the state-of-the-art performance of our approach.
A fundamental limitation of current visible through shortwave infrared hyperspectral imaging systems is the dependence on solar illumination. This reliance limits the operability of such systems to small windows during which the sun provides enough solar radiation to achieve adequate signal levels. Similarly, nighttime collection is infeasible. This work discusses the development and testing of a high-powered super-continuum laser for potential use as an on-board illumination source coupled with a hyperspectral receiver to allow for day/night operability. A 5-watt shortwave infrared supercontinuum laser was developed, characterized in the lab, and tower-tested along a 1.6km slant path to demonstrate propagation capability as a spectral light source.
A multi-modal (hyperspectral, multispectral, and LIDAR) imaging data collection campaign was conducted just south of Rochester New York in Avon, NY on September 20, 2012 by the Rochester Institute of Technology (RIT) in conjunction with SpecTIR, LLC, the Air Force Research Lab (AFRL), the Naval Research Lab (NRL), United Technologies Aerospace Systems (UTAS) and MITRE. The campaign was a follow on from the SpecTIR Hyperspectral Airborne Rochester Experiment (SHARE) from 2010. Data was collected in support of the eleven simultaneous experiments described here. The airborne imagery was collected over four different sites with hyperspectral, multispectral, and LIDAR sensors. The sites for data collection included Avon, NY, Conesus Lake, Hemlock Lake and forest, and a nearby quarry. Experiments included topics such as target unmixing, subpixel detection, material identification, impacts of illumination on materials, forest health, and in-water target detection. An extensive ground truthing effort was conducted in addition to collection of the airborne imagery. The ultimate goal of the data collection campaign is to provide the remote sensing community with a shareable resource to support future research. This paper details the experiments conducted and the data that was collected during this campaign.
A multi-modal (hyperspectral, LiDAR, and multi-spectral) imaging data collection campaign was conducted at
the Rochester Institute of Technology (RIT) in conjunction with SpecTIR, LLC, in the Rochester, New York, area
July 26-29, 2010. The campaign was titled the SpecTIR Hyperspectral Airborne Rochester Experiment (SHARE)
and collected data in support of nine simultaneous unique experiments, several of which leveraged data from
multiple modalities. Airborne imagery was collected over the city of Rochester with hyperspectral, multispectral,
and Light Detection and Ranging (LiDAR) sensors. Sites for data collection included the Genesee River, sections
of downtown Rochester, and the RIT campus. Experiments included sub-pixel target detection, water quality
monitoring, thermal vehicle tracking and wetlands health assessment. An extensive ground truthing effort was
accomplished in addition to the airborne imagery collected. The ultimate goal of this comprehensive data
collection campaign was to provide a community sharable resource that would support additional experiments.
This paper details the experiments conducted and the corresponding data that were collected in conjunction
with this campaign.
Various hyperspectral change detection methods exist in the literature. Here prediction-based methods, such as
chronochrome and covariance equalization, are reviewed and compared with a more recently developed model-based
approach. These methods are typically applied for anomalous change detection. Several methods for
extending these algorithms to achieve matched change detection are discussed. The algorithms are then applied
to airborne visible to near infrared hyperspectral data collected recently over Rochester, New York.
A new method for hyperspectral change detection derived from a parametric radiative transfer model was recently
developed. The model-based approach explicitly accounts for local illumination variations, such as shadows,
which act as a constant source of false alarms in traditional change detection techniques. Here we formally
derive the model-based approach as a generalized likelihood ratio test (GLRT) developed from the data model.
Additionally, we discuss variations on implementation techniques for the algorithm and provide results using
tower-based data and HYDICE data.
The majority of pixel-level hyperspectral change detection algorithms have risen out of probabilistic models
developed for the data. These algorithms typically operate in two stages. In the first stage, the illumination
differences and other changes due to atmospheric and environmental conditions between the two scenes are
removed. In the second stage, a hypothesis test is performed on the difference between these normalized pixels.
These particular change detection methods often suffer due to local variability within the data. As an alternative
to these statistical-based change detection algorithms, this paper examines the use of a parametric physical model
towards change detection. For a single hyperspectral data set, the number of unknown parameters in the model
is greater than the number of measurements. However, if a second data set exists and the underlying material reflectance of each pixel is assumed to remain constant between the two, one can develop a problem for which the number of measurements is greater than the number of unknowns allowing for application of standard constrained optimization methods for parameter estimation. Assuming the validity of the physical model used, any residual error remaining after obtaining the optimal parameter estimates must result from noise or a violation of the reflectance assumption made, i.e., a change in material reflectance from time-1 to time-2. Accordingly, the fit error for each pixel is an indicator of reflectance change. Additionally, the proposed framework allows for incorporating spatial information at some later point. This paper provides a preliminary look at the proposed change detection method and associated challenges.
The use of hyperspectral imaging is a fast growing field with many applications in the civilian, commercial and military sectors. Hyperspectral images are typically composed of many spectral bands in the visible and infrared regions of the electromagnetic spectrum and have the potential to deliver a great deal of information about a remotely sensed scene. One area of interest regarding hyperspectral images is anomaly detection, or the ability to find spectral outliers within a complex background in a scene with no a priori information about the scene or its specific contents. Anomaly detectors typically operate by creating a statistical background model of a hyperspectral image and measuring anomalies as image pixels that do not conform properly to that given model. In this study we compare the performance over diurnal and seasonal changes for several different anomaly detection methods found in the literature and a new anomaly detector that we refer to as the fuzzy cluster-based anomaly detector. Here we also compare the performance of several anomaly-based change detection algorithms. Our results indicate that all anomaly detectors tested in this experimentation exhibit strong performance under optimum illumination and environmental conditions. However, our results point toward a significant performance advantage for cluster-based anomaly detectors in the presence of adverse environmental conditions.
KEYWORDS: Error analysis, Scanning electron microscopy, Sensors, Detection and tracking algorithms, Vegetation, Data modeling, Signal processing, Reflectivity, Hyperspectral imaging, Algorithm development
Recent research in hyperspectral change detection has focused on the use of a single reference scene to identify
anomalous changes that may be present in the current test scene by suppressing stationary background using
various predictive algorithms. This paper extends such algorithms to the use of multiple reference scenes in
an attempt to improve change detection performance in hyperspectral images in what is called multi-temporal
change detection (MTCD). Often, an airborne hyperspectral sensor performs multiple reconnaissance passes over
a specific spatial area. Consequently, a multi-temporal hyperspectral data set may exist for a spatial area of
interest and the potential arises to improve clutter suppression and increase change detection performance by using
multiple references. This multi-temporal change detection method, of course, requires precise co-registration
of all the scenes being used as pixel level changes are being sought.
Ground-based hyperspectral data collected using an imaging spectrometer mounted on a pan and tilt is used to
perform this study. This method of collection helps ensure precise co-registration between scenes. The scenes
are collected over a period of many months and consequently have different illumination and vegetation conditions
present. These natural variations between scenes are not considered anomalous changes and should not be
targeted by algorithms. A detection scene is collected as well with an anomalous change introduced to allow for
testing. Various multi-temporal change detection approaches are derived using single-reference change detection
techniques and simple signal processing knowledge. These methods are discussed and compared using ROC
analysis on the data available.
An earlier paper [1] discussed the merits of adaptive coded apertures for use as lensless imaging systems in the thermal
infrared and visible. It was shown how diffractive (rather than the more conventional geometric) coding could be used,
and that 2D intensity measurements from multiple mask patterns could be combined and decoded to yield enhanced
imagery. Initial experimental results in the visible band were presented. Unfortunately, radiosity calculations, also
presented in that paper, indicated that the signal to noise performance of systems using this approach was likely to be
compromised, especially in the infrared.
This paper will discuss how such limitations can be overcome, and some of the tradeoffs involved. Experimental results
showing tracking and imaging performance of these modified, diffractive, adaptive coded aperture systems in the visible
and infrared will be presented. The subpixel imaging and tracking performance is compared to that of conventional
imaging systems and shown to be superior. System size, weight and cost calculations indicate that the coded aperture
approach, employing novel photonic MOEMS micro-shutter architectures, has significant merits for a given level of
performance in the MWIR when compared to more conventional imaging approaches.
This paper covers the impact of registration errors between two images on chronochrome and covariance equalization
predictors used for hyperspectral change detection. Hyperspectral change detection involves the comparison of data
collected of the same spatial scene on two different occasions to try to identify anomalous man-made changes. Typical
change detection techniques employ a linear prediction method followed by a subtraction step to identify changes. These
linear predictors rely upon statistics from both scenes to determine a respective gain and offset. Chronochrome and
covariance equalization remain two common predictors used in the change detection process. Chronochrome relies upon
a cross-covariance matrix for prediction whereas covariance equalization relies solely upon the individual covariance
matrices. In theory, chronochrome seems more susceptible to image misregistration issues as joint statistic estimates may
suffer with registration error present. This paper examines the validity of this assumption. Using a push-broom style
imaging spectrometer mounted on a pan and tilt, visible to near infrared data of scenes suitable for change detection
analysis are gathered. The pan and tilt system ensures initial misregistration of the data is minimal. Using simple
translations of the scenes, misregistration impacts upon prediction error and change detection are examined for varying
degrees of shift.
Diffractive optical systems in the Infrared (IR) wavelength regime are being re-examined for remote sensing
applications. A pupil-plane adaptive coded aperture can enable a fine resolution, wide field of view sensor system
without mechanical scanning. Due to the relatively long wavelengths, coded aperture systems in the IR have unique
issues in regards to e.g. X-ray coded apertures. These include diffraction effects, wavelength dependence of optical
elements, off axis aberrations due to thick screens, etc. In this paper, we provide a general system model framework
based on partial coherence theory that enables us to explore many of the technical challenges in IR diffractive
imaging. This paper develops the general theory and shows examples of issues that impact the optical transfer
function (OTF) and impulse response of these types of architectures.
Hyperspectral change detection has been shown to be a promising approach for detecting subtle targets in complex
backgrounds. Reported change detection methods are typically based on linear predictors that assume a space-invariant
affine transformation between image pairs. Unfortunately, several physical mechanisms can lead to significant space
variance in the spectral change associated with background clutter, including shadowing and other illumination
variations as well as seasonal impacts on the spectral nature of vegetation, and this can lead to poor change detection
performance. This paper outlines a methodology to deal with such space-variant change using spectral clustering and
other related least-squares optimization techniques. Several specific algorithms are developed and applied to change
imagery captured under controlled conditions, and the impacts on clutter suppression are quantified and compared. The
results indicate that such techniques can provide markedly increased clutter suppression and change detection
performance when the environmental conditions associated with the image pairs are substantially different.
The use of hyperspectral imaging (HSI) technology to support a variety of civilian, commercial, and military remote
sensing applications, is growing. The rich spectral information present in HSI allows for more accurate ground cover
identification and classification than with panchromatic or multispectral imagery. One class of problems where
hyperspectral images can be exploited, even when no a priori information about a particular ground cover class is
available, is anomaly detection. Here spectral outliers (anomalies) are detected based on how well each hyperpixel
(spectral irradiance vector for a given pixel position) fits within some background statistical model. Spectral anomalies
may correspond to areas of interest in a given scene. In this work, we compare several anomaly detectors found in the
literature in novel experiments. In particular, we study the performance of the anomaly detectors in detecting several
man-made painted panels in a natural background using visible/near-infrared hyperspectral imagery. The data have been
collected over the course of a nine month period, allowing us to test the robustness of the anomaly detectors with
seasonal change. The detectors considered include the simple Gaussian anomaly detector, a Gaussian mixture model
(GMM) anomaly detector, and the cluster-based anomaly detector (CBAD). We examine the effect of the number of
components for the GMM and the number of clusters for the CBAD. Our preliminary results suggest that the use of a
CBAD yields the best results for our data.
This study examines the effectiveness of specific hyperspectral change detection algorithms on scenes with different
illumination conditions such as shadows, low sun angles, and seasonal vegetation changes with specific emphasis placed
on background suppression. When data sets for the same spatial scene on different occasions exist, change detection
algorithms utilize linear predictors such as chronochrome and covariance equalization in an attempt to suppress
background and improve detection of atypical manmade changes. Using a push-broom style imaging spectrometer
mounted on a pan and tilt platform, visible to near infrared data sets of a scene containing specific objects are gathered.
Hyperspectral system characterization and calibration is performed to ensure the production of viable data. Data
collection occurs over a range of months to capture a myriad of conditions including daily illumination change, seasonal
illumination change, and seasonal vegetation change. Choosing reference images, the degree of background suppression
produced for various time-2 scene conditions is examined for different background classes. A single global predictor
produces a higher degree of suppression when the conditions between the reference and time-2 remain similar and
decreases as drastic illumination and vegetation alterations appear. Manual spatial segmentation of the scene coupled
with the application of a different linear predictor for each class can improve suppression.
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