The amount of hyperspectral imagery (HSI) data currently available is relatively small compared to other imaging modalities, and what is suitable for developing, testing, and evaluating spatial-spectral algorithms is virtually nonexistent. In this work, a significant amount of coincident airborne hyperspectral and high spatial resolution panchromatic imagery that supports the advancement of spatial-spectral feature extraction algorithms was collected to address this need. The imagery was collected in April 2013 for Ohio University by the Civil Air Patrol, with their Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) sensor. The target materials, shapes, and movements throughout the collection area were chosen such that evaluation of change detection algorithms, atmospheric compensation techniques, image fusion methods, and material detection and identification algorithms is possible. This paper describes the collection plan, data acquisition, and initial analysis of the collected imagery.
The application of compression to hyperspectral image data is a significant technical challenge. A primary bottleneck in disseminating data products to the tactical user community is the limited communication bandwidth between the airborne sensor and the ground station receiver. This report summarizes the newly-developed “Z-Chrome” algorithm for lossless compression of hyperspectral image data. A Wiener filter prediction framework is used as a basis for modeling new image bands from already-encoded bands. The resulting residual errors are then compressed using available state-of-the-art lossless image compression functions. Compression performance is demonstrated using a large number of test data collected over a wide variety of scene content from six different airborne and spaceborne sensors .
Proc. SPIE. 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
KEYWORDS: Target detection, Detection and tracking algorithms, Data modeling, Sensors, Digital filtering, Reflectivity, Feature selection, Target recognition, Hyperspectral target detection, RGB color model
A novel approach to VNIR hyperspectral target identification is presented based on the Least-Angle Regression (LARS)
variable selection and model building algorithm. The problem to be solved is that of accurately identifying a target's
primary signature component given a sub-pixel observation. Traditional matched detectors (MF, ACE, etc.) perform
well at discriminating a target from a random cluttered background, but do not perform so well at unambiguously
matching an observation with its counterpart in a large spectral library containing thousands of signatures. The LARS
model-building algorithm efficiently selects a parsimonious subset of a large ensemble of model terms to optimally
describe a particular target observation. The LARS solution technique is a recent addition to the family of model
selection algorithms that includes Stepwise Regression, Forward Selection, and Backward Elimination. LARS is
particularly well-suited to this problem as it is easily modified to enforce material abundance constraints: positive
coefficients that sum to unity. Other approaches generally enforce such constraints in an ad-hoc fashion or use
computationally demanding nonlinear programming solution techniques. LARS enforces these constraints as an inherent
property of the model while remaining as computationally efficient as traditional sequential linear least-squares solvers.
We demonstrate and quantify sub-pixel material identification performance using simulated target observations tested
against large signature libraries.
A new endmember finder and spectral unmixing algorithm based on the LARS/Lasso method for linear regression
is developed. The endmember finder is sequential; a single endmember is identified at first and further
endmembers which depend on the previous ones are found. The process terminates once a pre-determined number
of endmembers have been found, or when the modeling error has attained the noise floor. The unmixing
algorithm is a straightforward procedure that expresses each pixel as a linear combination of endmembers in a
physically meaningful way. This algorithm successfully unmixes simulated data, and shows promising results on
real hyperspectral images as well.
An adaptive algorithm is described for deriving constant false alarm rate (CFAR) detection thresholds based on
statistically motivated models of actual spectral detector output distributions. The algorithm dynamically tracks the
distribution of detector observables and fits the observed distribution to a suitable mixture density model function. The
fitted distribution model is used to compute numerical detection thresholds that achieve a constant probability of false
alarm (Pfa) per pixel. Typically gamma mixture densities are used to model outputs of anomaly detectors based on
quadratic decision statistics, while normal mixture densities are used for linear matched filter type detectors. In order to
achieve the computational efficiency required for real-time implementations of the algorithm on mainstream
microprocessors, a robust yet considerably less complex exponential mixture model was recently developed as a general
approximation to common long-tailed detector distributions. Within the region of operational interest, namely between
the primary mode and the far tail, this approximation serves as an accurate model while providing significant reduction
in computational cost. We compare the performance of the exponential approximation against the full-blown gamma
and normal models. We also demonstrate the false alarm regulation performance of the adaptive CFAR algorithm using
anomaly and matched detector outputs derived from actual VNIR-band hyperspectral imagery collected by the Civil Air
Patrol (CAP) Airborne Real time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) system.
The Civil Air Patrol (CAP) is procuring Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) systems to increase their search-and-rescue mission capability. These systems are being installed on a fleet of Gippsland GA-8 aircraft, and will position CAP to gain realworld mission experience with the application of hyperspectral sensor and processing technology to search and rescue. The ARCHER system design, data processing, and operational concept leverage several years of investment in hyperspectral technology research and airborne system demonstration programs by the Naval Research Laboratory (NRL) and Air Force Research Laboratory (AFRL). Each ARCHER system consists of a NovaSol-designed, pushbroom, visible/near-infrared (VNIR) hyperspectral imaging (HSI) sensor, a co-boresighted visible panchromatic high-resolution imaging (HRI) sensor, and a CMIGITS-III GPS/INS unit in an integrated sensor assembly mounted inside the GA-8 cabin. ARCHER incorporates an on-board data processing system developed by Space Computer Corporation (SCC) to perform numerous real-time processing functions including data acquisition and recording, raw data correction, target detection, cueing and chipping, precision image geo-registration, and display and dissemination of image products and target cue information. A ground processing station is provided for post-flight data playback and analysis. This paper describes the requirements and architecture of the ARCHER system, with emphasis on data processor design, components, software, interfaces, and displays. Key sensor performance characteristics and real-time data processing features are discussed. The use of the system for detecting and geo-locating ground targets in real-time is demonstrated using test data collected in Southern California in the fall of 2004.
The Civil Air Patrol (CAP) is procuring Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) systems to increase their search-and-rescue mission capability. These systems are being installed on a fleet of Gippsland GA-8 aircraft, and will position CAP to gain realworld mission experience with the application of hyperspectral sensor and processing technology to search and rescue. The ARCHER system design, data processing, and operational concept leverage several years of investment in hyperspectral technology research and airborne system demonstration programs by the Naval Research Laboratory (NRL) and Air Force Research Laboratory (AFRL). Each ARCHER system consists of a NovaSol-designed, pushbroom, visible/near-infrared (VNIR) hyperspectral imaging (HSI) sensor, a co-boresighted visible panchromatic high-resolution imaging (HRI) sensor, and a CMIGITS-III GPS/INS unit in an integrated sensor assembly mounted inside the GA-8 cabin. ARCHER incorporates an on-board data processing system developed by Space Computer Corporation (SCC) to perform numerous real-time processing functions including data acquisition and recording, raw data correction, target detection, cueing and chipping, precision image geo-registration, and display and dissemination of image products and target cue information. A ground processing station is provided for post-flight data playback and analysis. This paper describes the requirements and architecture of the ARCHER system, including design, components, software, interfaces, and displays. Key sensor performance characteristics and real-time data processing features are discussed in detail. The use of the system for detecting and geo-locating ground targets in real-time is demonstrated using test data collected in Southern California in the fall of 2004.
Robust, timely, and remote detection of mines and minefields is central to both tactical and humanitarian demining efforts, yet remains elusive for single-sensor systems. Here we present an approach to jointly exploit multisensor data for detection of mines from remotely sensed imagery. LWIR, MWIR, laser, multispectral, and radar sensor have been applied individually to the mine detection and each has shown promise for supporting automated detection. However, none of these sources individually provides a full solution for automated mine detection under all expected mine, background and environmental conditions. Under support from Night Vision and Electronic Sensors Directorate (NVESD) we have developed an approach that, through joint exploitation of multiple sensors, improves detection performance over that achieved from a single sensor. In this paper we describe the joint exploitation method, which is based on fundamental detection theoretic principles, demonstrate the strength of the approach on imagery from minefields, and discuss extensions of the method to additional sensing modalities. The approach uses pre-threshold anomaly detector outputs to formulate accurate models for marginal and joint statistics across multiple detection or sensor features. This joint decision space is modeled and decision boundaries are computed from measured statistics. Since the approach adapts the decision criteria based on the measured statistics and no prior target training information is used, it provides a robust multi-algorithm or multisensor detection statistic. Results from the joint exploitation processing using two different imaging sensors over surface mines acquired by NVESD will be presented to illustrate the process. The potential of the approach to incorporate additional sensor sources, such as radar, multispectral and hyperspectral imagery is also illustrated.
For hyperspectral remote sensing, the physics-based transformation connecting two multivariate sets of spectral radiance data of the same scene collected at two disparate times is approximately linear (plus an offset). Generally, the covariance structures of two such data sets provide partial information about any linear transformation connecting them. The remaining unknown degrees of freedom of the transformation must be deduced from other statistics, or from a knowledge of the underlying phenomenology. Among all the possible transformations consistent with measured pairs of hyperspectral covariance structures, a particularly simple and accurate one has been found. This "rotation free" flavor of "Covariance Equalization" (CE) has led to a simplified signal processing architecture that has been implemented in a real time VNIR hyperspectral target detection system. This paper describes that architecture, presents detection performance results, and introduces a new algorithm for long-interval change detection, Matched Change Detection.
Multi-pass search and reconnaissance missions provide unique opportunities for hyperspectral target detection systems to operate at drastically reduced false alarm rates. In the simplest example, confirmed false alarms generated by an anomaly detector on an initial pass can be archived for future reference. A more sophisticated approach captures false alarm signatures to update background clutter models that inform detection algorithms. Or if detections are confirmed as targets on one pass, then matched-filter maps based on their spectra can be compared over time to monitor changes. As a final example, when sub-pixel registration is feasible, multi-temporal spectral covariance relations can be estimated from the data and used to detect anomalous changes at low false alarm rates, using no target signature information. All but the simplest of such methods require that the spectral evolution of a terrestrial background -- its chromodynamics -- be modeled sufficiently that naturally occurring changes are not confused with unnatural ones. This paper describes several detection paradigms that rely on multi-pass missions. Optimal linear algorithms to predict scene and target evolution are discussed, as are more realistic methods with relaxed operational requirements. One of these, called <i>Covariance Equalization</i>, is shown to perform nearly as well as the minimum error solution based on the matrix Wiener filter, which requires subpixel registration accuracy.
The theory of asymptotic eigenvalue distributions of sample covariance matrices has been applied to array processing and model identification problems that require characterization of signal and noise modes in vector-valued observations. It naturally applies in cases where the dimensionality of the observation space is large compared with the signal model order. A similar situation holds for most hyperspectral image observations. Hyperspectral data is frequently described in terms of a "signal" component composed of linear combinations of endmember basis spectra, plus random additive "noise" from the sensor and environment. The number of resolvable signal modes is typically much smaller than the number of spectral bands, and most of the orthogonal spectral dimensions generated by a principal components analysis are dominated by noise.
Analytical characterization of the "noise eigenmodes" of a hyperspectral data cube supports the development of objective methods for estimating image noise statistics, signal-to-noise ratio, and the complexity and content of the underlying spectral scene. This paper reviews some fundamental results in eigenvalue distribution theory for high-dimensional data, and explores potential applications of the theory to hyperspectral data analysis. Specific applications developed and illustrated in the paper include scene-based estimation of noise-equivalent spectral radiance (NESR), and automated selection of signal-bearing and noise-limited subspaces for spectral analysis.
Unsupervised classification of multispectral and hyperspectral data is useful for a range of military and commercial remote sensing applications. These include terrain categorization, material detection and identification, and land use quantification. Here we show the development and application of an adaptive Gaussian Spectral Clustering approach to unsupervised classification of hyperspectral data. The method is built on adaptively estimating the parameters of a Gaussian mixture model from over local regions, and includes methods for adjusting to inevitable non-stationarity of hyperspectral image data. The algorithm is suitable for application to streaming hyperspectral data as would be required for real-time applications. In this paper we outline the model used, estimation techniques, and methods for adaptively estimating key model parameters required to characterize hyperspectral imagery. The key elements of the approach are demonstrated on reflective band hyperspectral data from NRL WarHORSE and NASA AVIRIS hyperspectral imagery.
The standard approach to solving detection problems in which clutter and/or target ditributions are modeled with unknown parameter is to apply the generalized likelihood ratio (GLR) test. This procedure automatically gernerates new estimates of the unknown model parameter for each new feature test value. An alternative approach is to estimate prior distribution for the unknown parameters. The associated Bayesian Likelihood Ratio (BLR) test can be used to generate many standard detectors for example, matched filtering or the GLR as special cases. For the particular problem of Joint Subspace Detection (JSD), several such Bayesian problems often lead to the same test as some GLR problem. Formulating such problems can lend insight into what types of background and target distributions are appropriate for a given GLR test. In addition, the added generality afforded by the new approach, in the form a selectable prior distributions, defines a wider exploratory space fro target detection. JSD can, for example, permit the incorporation of general types of experience gleaned from measurement programs. This paper explores these potentialities by applying several Bayesian formulations of the detection problem to hyperspectral data set.
Space Computer Corporation has developed an innovative atmospheric retrieval algorithm called OPRA (Oblique Projection Retrieval of the Atmosphere). This algorithm is designed to retrieve both path radiance and atmospheric transmissivity directly from calibrated LWIR radiance spectra through a two-stage application of oblique projection operators. The OPRA method assumes the surface in the pixel field of view has an emissivity close to unity. Under this condition, the sensed radiance can be accurately modeled as the blackbody ground radiance attenuated by a multiplicative transmissivity and enhanced by an additive path radiance. The oblique projection operator is defined in terms of a range space H and a null space S. The subspaces H and S are independent, although not necessarily orthogonal. The properties of the operator are such that when it is applied to a measured signal all components spanned by the null space S are eliminated, while those spanned by the range space H are preserved. Stage 1 of OPRA nullifies the surface radiance multiplied by the transmissivity and retrieves the path radiance. Stage 2 is applied to the logarithm of the measured signal minus the retrieved path radiance to nullify the log of the Planck function and thereby retrieve the log of the transmissivity. The OPRA algorithm has been applied to both model data and SEBASS LWIR data and initial results indicate that atmospheric retrieval errors are sensitive to instrument artifacts not included in the various subspace definitions.
Numerous statistical approaches have been developed for small target detection in cluttered environments. Examples include orthogonal background suppression (OBS) where the initial principal components are suppressed, and the clutter matched filter (CMF) where the principal components are weighted by the inverse of the eigenvalues and the latter principal components are discarded. Our research has shown that improved target detection performance can be obtained by combining certain aspects of both OBS and CMF approaches. This is especially true in the presence of limited scene data (finite number of pixels) or an imperfect reference target spectrum. The basis of this idea is to use weighting by the inverse of the eigenvalues (from CMF) for the initial PCs and the uniform weighting for the later PCs (from OBS). Examples of this new technique and comparisons with OBS and CMF will be shown with model data with realistic clutter containing a chemical plume.
The AHI (Airborne Hyperspectral Imager) system was designed to detect the presence of buried land mines from the air through detection of along wave IR observable associated with mine installation. The system is a helicopter-borne LWIR hyperspectral imager with real time on-board radiometric calibration and mine detection. It collects hyperspectral imagery from 7.5 to 11.5 μm in either 256 or 32 spectral bands. At all wavelengths the AHI noise equivalent delta (NEΔT) temperature is less than 0.1K at 300K and the NESR is less than .02 watts/m<SUP>2</SUP>-sr-μm.
Hyperspectral images are frequently analyzed in terms of the linear mixing model, which assumes that observed pixel radiances are generated by linear combinations of a relatively small number of spectral constituent signatures. The constituents are generally modeled as deterministic points in color space whose locations can in principle be found by exploiting the convex geometry of the mixture simplex. This paper presents an alternative stochastic mixing model (SMM) that associates scene constituents with distinct probability distributions,the parameters of which are estimated from observed data using statistical clustering methods. By defining distributions corresponding to both constituent and mixed pixel classes, the SMM can often be used to generate physically meaningful classification maps of spectrally-heterogeneous scenes. However, the most significant application of the stochastic approach is to hyperspectral target detection based on statistical decision theory concepts. A SMM can provide accurate parametric estimates of the spectral distributions for mixed scenes, thereby improving the performance of hypothesis testing procedures that utilize replacement targets with spectral signature uncertainty. SMM principles and applications are illustrated using hyperspectral imagery collected by the LIFTIRS and HYDICE instruments.
Multivariate correlation techniques can be used to enhance target contrast in spectroscopic imagery. But in most cases the detectability of dim targets remains limited by residual background clutter. If, however, multiple-time measurements can be made, detection performance can be markedly enhanced by an integrated spectral/temporal technique that exploits the correlated nature of background spectral trajectories. We demonstrate the detection of extreme subpixel objects, such as is required by long-range remote sensing systems. We also show that the time intervals between data collections can be long. The confusing effects of natural background evolution-in temperature distribution or illumination-can be distinguished from anomalous changes. Data collected with longwave infrared point- and imaging-spectrometers have validated the concept.
Under the sponsorship of the DARPA Hyperspectral Mine Detection program, a series of both non-imaging and imaging experiments have been conducted to explore the physical basis of buried object detection in the visible through thermal infrared. Initially, non-imaging experiments were performed at several geographic locations. Potential spectral observables for detection of buried mines in the thermal portion of the infrared were found through these measurements. Following these measurements with point spectrometers, a series of hyperspectral imaging measurements was conducted during the summer of 1995 using the SMIFTS instrument from the University of Hawaii and the LIFTIRS instrument from Lawrence Livermore National Laboratory. The SMIFTS instrument (spatially modulated imaging Fourier transform spectrometer) acquires hyperspectral image cubes in the short-wave and mid-wave infrared and LIFTIRS (Livermore imaging Fourier transform infrared spectrometer) acquires hyperspectral image cubes in the long-wave infrared. Both instruments were optimized through calibration to maximize their signal to noise ratio and remove residual sensor pattern. The experiments were designed to both explore further the physics of disturbed soil detection in the infrared and acquire image data to support the development of detection algorithms. These experiments were supported by extensive ground truth, physical sampling and laboratory analysis. Promising detection observables have been found in the long-wave infrared portion of the spectrum. These spectral signatures have been seen in all geographical locations and are supported by geological theory. Data taken by the hyperspectral imaging sensors have been directly input to detection algorithms to demonstrate mine detection techniques. In this paper, both the non-imaging and imaging measurements made to date will be summarized.
Infrared multispectral sensors are being investigated as a means for day and night target detection. Infrared multispectral sensors would exploit high spectral band-to-band correlation to reject high background clutter. An infrared Fourier transform spectrometer-based field measurement system was used to collect spectral signature data of targets and desert scrub and sand backgrounds from a 100 foot tower at White Sands Missile Range. The measurements include target-to-background spectral contrast, subpixel targets, background spectral correlation, and background spatial power spectra. The measurements have been analyzed to determine multispectral signal-to-clutter ratios versus target, background, diurnal, and weather variations, background correlation versus temperature clutter variations, and spectral correlation versus spatial scale. These measurements contribute to the expanding target and background infrared hyperspectral signature database. The results of the analysis demonstrate the utility and robustness of infrared multispectral techniques for target detection.
A series of infrared hyperspectral field measurements was made at Wright Patterson Air Force Base and the U.S. Army White Sands Missile Range by the Joint Multispectral Program (JMSP) between November 1993 and June 1994. In these experiments, a highly sensitive Fourier transform spectrometer (FTS) was used to collect data from test panels, military and civilian vehicles, and various types of natural backgrounds. Post-collection data analyses are being conducted by the JMSP to assess the potential of thermal multispectral processing for detecting and classifying low-contrast ground targets in natural clutter environments. One target material of special interest is CARC paint, which is currently applied to U.S. Army vehicles in various colors to create woodland and desert camouflage patterns. CARC-painted test panels were observed in a wide variety of backgrounds and weather conditions during all of the JMSP experiments. It is shown here that certain fine-scale spectral features of this paint can support reliable two-color discrimination of CARC-coated test panels in different natural backgrounds, even under low contrast and high clutter conditions. The paper also examines environmental variations in two key parameters that determine spectral detectability; specifically, the observed target-background spectral contrast signature (which provides the required coloring), and the background spectral correlation (which provides for multiband clutter suppression).
Thermal infrared multi-spectral field measurements of test panels, military vehicles, and backgrounds were extensively analyzed to assess the potential of multi-spectral processing for detecting low-contrast ground targets in vegetation clutter. The measurements clearly show the existence of exploitable color due to fine-scale variations in target-background spectral contrast, and they establish environment limits on coherent multi-band clutter suppression based on background spectral correlation. Typical variations in key multi-spectral performance parameters, and their implications for waveband selection, sensor design, and robust target detection performance, are presented and discussed.
This paper presents a new, self-adaptive technique for the correlation of non-uniformities (fixed-pattern noise) in high-density infrared focal-plane detector arrays. We have developed a new approach to non-uniformity correction in which we use multiple image frames of the scene itself, and take advantage of the aim-point wander caused by jitter, residual tracking errors, or deliberately induced motion. Such wander causes each detector in the array to view multiple scene elements, and each scene element to be viewed by multiple detectors. It is therefore possible to formulate (and solve) a set of simultaneous equations from which correction parameters can be computed for the detectors. We have tested our approach with actual images collected by the ARPA-sponsored MUSIC infrared sensor. For these tests we employed a 60-frame (0.75-second) sequence of terrain images for which an out-of-date calibration was deliberately used. The sensor was aimed at a point on the ground via an operator-assisted tracking system having a maximum aim point wander on the order of ten pixels. With these data, we were able to improve the calibration accuracy by a factor of approximately 100.
Proc. SPIE. 1481, Signal and Data Processing of Small Targets 1991
KEYWORDS: Target detection, Signal to noise ratio, Image processing, Digital filtering, Data processing, Signal processing, Image filtering, Electronic filtering, Binary data, Filtering (signal processing)
The velocity filter (or 3-D matched filter) is known to be a powerful signal processing technique for detecting and tracking weak moving objects in electro-optical image sequences. To date, however, its application has been limited by the enormous amounts of hardware required to implement the large 'filter banks' that are needed to cover the prior uncertainty in apparent target velocity. This paper presents the results of an algorithm and architecture study that explored ways of significantly reducing the real-time hardware required to obtain a specified level of performance with the velocity filter approach. The most effective solution, based on an optimum single-bit velocity filter implemented in a special-purpose bit serial processor, is capable of achieving extremely high filter computation rates on a single semi- custom VLSI chip. A real-time brassboard implementation of this architecture, the Velocity Filter Processor, is currently under development at Space Computer Corporation.
This paper describes adaptive signal processing techniques that utilize spectral and temporal information provided by passive infrared imaging sensors to enhance the detectability of sub- pixel targets in clutter. The approaches are directly applicable to advanced sensors like the DARPA-sponsored MUSIC instrument, which are capable of collecting multi-spectral frame sequences in the thermal infrared region. The performance of several algorithm concepts is demonstrated by processing dual-band frame sequence data taken by the MUSIC sensor. The examples also demonstrate the importance of accurate frame registration prior to multiple- image signal processing.
This paper describes the evolutionary development of adaptive signal processing algorithms which utilize
spatial and spectral information provided by a passive infrared sensor to enhance the detectability of targets in
clutter. Key parameters affecting the performance of multi-spectral detection processors are identified and
discussed. Adaptive filtering algorithms are presented which can achieve near-optimum detection performance
with no prior knowledge of the target and background spectral properties.