Field measurement campaigns typically deploy numerous sensors having different sampling characteristics for spatial,
temporal, and spectral domains. Data analysis and exploitation is made more difficult and time consuming as the sample
data grids between sensors do not align. This report summarizes our recent effort to demonstrate feasibility of a processing
chain capable of “fusing” image data from multiple independent and asynchronous sensors into a form amenable to
analysis and exploitation using commercially-available tools.
Two important technical issues were addressed in this work: 1) Image spatial registration onto a common pixel grid, 2)
Image temporal interpolation onto a common time base. The first step leverages existing image matching and registration
algorithms. The second step relies upon a new and innovative use of optical flow algorithms to perform accurate temporal
upsampling of slower frame rate imagery. Optical flow field vectors were first derived from high-frame rate, high-resolution
imagery, and then finally used as a basis for temporal upsampling of the slower frame rate sensor’s imagery.
Optical flow field values are computed using a multi-scale image pyramid, thus allowing for more extreme object motion.
This involves preprocessing imagery to varying resolution scales and initializing new vector flow estimates using that
from the previous coarser-resolution image.
Overall performance of this processing chain is demonstrated using sample data involving complex too motion observed
by multiple sensors mounted to the same base. Multiple sensors were included, including a high-speed visible camera, up
to a coarser resolution LWIR camera.
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 .
In EO tracking, target spatial and spectral features can be used to improve performance since they help distinguish the
targets from each other when confusion occurs during normal kinematic tracking. In this paper we introduce a method
to encode a target's descriptive spatial information into a multi-dimensional signature vector, allowing us to convert the
problem of spatial template matching into a form similar to spectral signature matching. This allows us to leverage
multivariate algorithms commonly used with hyperspectral data to the problem of exploiting panchromatic imagery. We
show how this spatial signature formulation naturally leads to a hybrid spatial-spectral descriptor vector that supports
exploitation using commonly-used spectral algorithms.
We introduce a new descriptor called Spectral DAISY for encoding spatial information into a signature vector, based on
the concept of the DAISY dense descriptor. We demonstrate the process on real data and show how the combined
spatial/spectral feature can be used to improve target/track association over spectral or spatial features alone.
The LWIR microgrid Polarized InfraRed Advanced Tactical Experiment (PIRATE) sensor was used to image
several types of RC model aircraft at varying ranges and speeds under different background conditions. The data
were calibrated and preprocessed using recently developed microgrid processing algorithms prior to estimation
of the thermal (s0) and polarimetric (s1 and s2) Stokes vector images. The data were then analyzed to assess the
utility of polarimetric information when the thermal s0 data is augmented with s1 and s2 information for several
model aircraft detection and tracking scenarios. Multi-variate analysis tools were applied in conjunction with
multi-hypothesis detection schemes to assess detection performance of the aircraft under different background
clutter conditions. We find that polarization is able to improve detection performance when compared with
the corresponding thermal data in nearly all cases. A tracking algorithm was applied to a sequence of s0 and
corresponding degree of linear polarization (DoLP) images. An initial assessment was performed to determine
whether polarization information can provide additional utility in these tracking scenarios.
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.
KEYWORDS: Atmospheric modeling, Atmospheric sensing, Black bodies, Space operations, Sensors, Data modeling, Long wavelength infrared, Atmospheric propagation, Ozone, Algorithm development
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.
The Multispectral Thermal Imager (MTI) is a satellite system developed by the DoE. It has 10 spectral bands in the reflectance domain and 5 in the thermal IR. It is pointable and, at nadir, provides 5m IFOV in four visible and short near IR bands and 20m IFOV at longer wavelengths. Several of the bands in the reflectance domain were designed to enable quantitative compensation for aerosol effects and water vapor (daytime). These include 3 bands in and adjacent to the 940nm water vapor feature, a band at 1380nm for cirrus cloud detection and a SWIR band with small atmospheric effects. The concepts and development of these techniques have been described in detail at previous SPIE conferences and in journals. This paper describes the adaptation of these algorithms to the MTI automated processing pipeline (standardized level 2 products) for retrieval of aerosol optical depth (and subsequent compensation of reflectance bands for calibration to reflectance) and the atmospheric water vapor content (thermal IR compensation). Input data sources and flow are described. Validation results are presented. Pre-launch validation was performed using images from the NASA AVIRIS hyperspectral imaging sensor flown in the stratosphere on NASA ER-2 aircraft compared to ground based sun photometer and radiosonde measurements from different sources. These data sets span a range of environmental conditions.
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 Multispectral Thermal Imager (MTI) has a number of core science retrievals which will be described. We will concentrate on describing the major Level-2 algorithms which cover land, water and atmospheric products. The land products comprise atmospherically corrected surface reflectances, vegetation health status, material identification, land temperature and emissivities. The water related products are: water mask, water quality and water temperature. The atmospheric products are: cloud mask, cirrus mask and atmospheric water vapor. We will present several of these algorithms and present results from simulated MTI data derived from AVIRIS and MODIS Airborne Simulator (MAS). An interactive analysis tool has been created to visually program and test certain Level-2 retrievals.
KEYWORDS: Calibration, Databases, Data processing, Image processing, Data modeling, Data acquisition, Algorithm development, Vegetation, Atmospheric modeling, Data storage
The major science goal for the Multispectral Thermal Imager (MTI) project is to measure surface properties such as vegetation health, temperatures, material composition and others for characterization of industrial facilities and environmental applications. To support this goal, this program has several coordinated components, including modeling, comprehensive ground-truth measurements, image acquisition planning, data processing and data interpretation. Algorithms have been developed to retrieve a multitude of physical quantities and these algorithms are integrated in a processing pipeline architecture that emphasizes automation, flexibility and robust operation. In addition, the MTI science team has produced detailed site, system and atmospheric models to aid in system design and data analysis. This paper will provide an introduction to the data processing and science algorithms for the MTI project. Detailed discussions of the retrieval techniques will follow in papers from the balance of this session.
Deriving information about the Earth's surface requires atmospheric corrections of the measured top-of-the- atmosphere radiances. One possible path is to use atmospheric radiative transfer codes to predict how the radiance leaving the ground is affected by the scattering and attenuation. In practice the atmosphere is usually not well known and thus it is necessary to use more practical methods. We will describe how to find dark surfaces, estimate the atmospheric optical depth, estimate path radiance and identify thick clouds using thresholds on reflectance and NDVI and columnar water vapor. We describe a simple method to correct a visible channel contaminated by a thin cirrus clouds.
We present a preliminary analysis and design framework developed for the evaluation and optimization of infrared, Imaging Spatial Heterodyne Spectrometer (SHS) electro-optic systems. Commensurate with conventional interferometric spectrometers, SHS modeling requires an integrated analysis environment for rigorous evaluation of system error propagation due to detection process, detection noise, system motion, retrieval algorithm and calibration algorithm. The analysis tools provide for optimization of critical system parameters and components including: (1) optical aperture, f-number, and spectral transmission, (2) SHS interferometer grating and Littrow parameters, and (3) image plane requirements as well as cold shield, optical filtering, and focal-plane dimensions, pixel dimensions and quantum efficiency, (4) SHS spatial and temporal sampling parameters, and (5) retrieval and calibration algorithm issues.
We present the Infrared Imaging Spatial Heterodyne Spectrometer (IRISHS) experiment. IRISHS is a new hyperspectral imaging spectrometer for remote sensing being developed by Los Alamos National Laboratory for use in identifying and assaying gases in the atmosphere when viewed against the Earth's background. The prototype instrument, which can operate between 8 and 11.5 micrometers (although the current IR camera operates from 8 - 9.5 micrometers), will be described. Imaging spatial heterodyne spectrometer technology is discussed in four companion papers also presented at this symposium.
KEYWORDS: 3D modeling, Atmospheric modeling, Sensors, Thermal modeling, Scene simulation, Data modeling, Monte Carlo methods, Fractal analysis, Ray tracing, FT-IR spectroscopy
Generating synthetic hyperspectral data cubes for the thermal is useful because it is very expensive to design and fly such sensors. Using synthetic da is useful in performing trade-off studies, e.g. spectral and radiometric performance requirements, and also in testing new algorithms, e.g. temperature emissivity separation. We have developed a method to simulate complex thermal scenes under variable solar illumination, different materials and including gas plumes. The IDL-based scene simulation tool consists of public domain tools for: 3D geometry generation, raytracing, spectral and thermal property libraries, atmospheric transmission and emission modeling. The data can then be used in standard hyperspectral processing programs or processed by special purpose programs. We show how this model can be used to generate synthetic data cubes for dispersive and Fourier transform IR spectrometers. The data in turn is then used to evaluate algorithms to separate temperature and emissivity.
The study of the Earth radiation budget is important for understanding the long-term impact of industrialization on the environment. Clouds exert the single most important influence on the Earth radiation budget. The ideal situation for predicting the effect of clouds is to have one of two extremes: either a cloud-free sky or a completely overcast sky. Each of these cases can easily be treated with a simple one-dimensional model. However, the more usual partly-cloudy sky condition introduces the possibility of an unlimited variety of cloud geometries, size and spatial distributions, and properties. In most of these cases the interaction among neighboring clouds presents a set of problems which cannot be accurately simulated with plane-parallel or even two-dimensional models. Only in recent years have researchers begun to understand the relationships between cloud three-dimensional effects and the anisotropy of Earth-reflected radiance. In the real world, the influence of clouds on radiation depends not only on liquid water content, or optical depth, but also on cloud microphysical properties such as particle shape, size distribution, and on cloud morphology and spatial distribution (Parol, et al., 1994). As shown by Stevens and Greenwald (1991), cloud morphology is likely to have a larger impact on the Earth radiation budget than cloud microphysics. Thus, it is very important to quantify and parameterize the influence of cloud inhomogeneities on the radiation field. The anisotropy of any radiation field can be studied by normalizing the emitted or reflected radiance by the equivalent Lambertian directional distribution of energy leaving the field. The work in this paper deals entirely with the shortwave portion of the spectrum; longwave emitted energy is not treated.
The goal of this research was to map the regimes of validity of the weak-line and strong-line limits as a function of temperature, pressure, and path length for the 2.7 micrometers and 6.3 micrometers H2O absorption bands. These calculations were done using an updated version of the NASA band model. A parametric study was performed where the error in assuming the validity of the two limits was calculated as a function of the physical parameters temperature, pressure, and path length. Results were generated in the form of spectral plots of the error and as band-integrated error presented in contour plots as a function of temperature and path length. Results indicate that for both bands, the weak-line error is localized in regions of intermediate temperatures and pressures at all path lengths. The strong-line limit error shows a linear increasing trend with pressure at short path lengths, while varying as a saddle-shaped function with respect to temperature and pressure at longer path lengths.
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