Target Detection in a Structured Background Environment Using an Infeasibility Metric in an Invariant Space

This paper develops a hybrid target detector that incorporates structured backgrounds and physics based modeling together with a geometric infeasibility metric. More often than not, detection algorithms are usually applied to atmospherically compensated hyperspectral imagery. Rather than compensate the imagery, we take the opposite approach by using a physics based model to generate permutations of what the target might look like as seen by the sensor in radiance space. The development and status of such a method is presented as applied to the generation of target spaces. The generated target spaces are designed to fully encompass image target pixels while using a limited number of input model parameters. Background spaces are modeled using a linear subspace (structured) approach characterized by endmembers found by using the maximum distance method (MaxD). After augmenting the image data with the target space, 15 endmembers were found, which were not related to the target (i.e., background endmembers). A geometric infeasibility metric is developed which enables one to be more selective in rejecting false alarms. Preliminary results in the design of such a metric show that an orthogonal projection operator based on target space vectors can distinguish between target and background pixels. Furthermore, when used in conjunction with an operator that produces abundance-like values, we obtained separation between target, ackground, and anomalous pixels. This approach was applied to HYDICE image spectrometer data

A Polarized Clutter Measurement Technique Based on the Governing Equation for Polarimetric Remote Sensing in the Visible to Near Infrared

Polarization adds another dimension to the spatial intensity and spectral information typically acquired in remote sensing. Polarization imparted by surface reflections contains unique and discriminatory signatures which may augment spectral target-detection techniques. While efforts have been made toward quantifying the polarimetric bidirectional reflectance distribution function (pBRDF) responsible for target material polarimetric signatures, little has been done toward developing a description of the polarized background or scene clutter. An approach is presented for measuring the pBRDF of background materials such as vegetation. The governing equation for polarized radiance reaching a sensor aperture is first developed and serves as a basis for understanding outdoor pBRDF measurements, as well as polarimetric remote sensing. The pBRDF measurements are acquired through an imaging technique which enables derivation of the BRDF variability as a function of the ground separation distance (GSD). An image subtraction technique is used to minimize measurement errors resulting from the partially polarized downwelled sky radiance. Quantifying the GSD-dependent BRDF variability is critical for background materials which are typically spatially inhomogeneous. Preliminary results from employing the measurement technique are presented

Image Quality Analysis of a Spectra-Radiometric Sparse-Aperture Model

Sparse aperture (SA) telescopes represent a promising technology to increase the effective diameter of an optical system while reducing overall weight and stowable size. Although conceptually explored in the literature for decades, the technology has only recently matured to the point of being reasonably considered for certain applications. In general, a sparse aperture system consists of an array of sub-apertures that are phased to synthesize a larger effective aperture. The models used to date to create predictions of sparse aperture imagery typically make use of a “gray world” assumption, where the input is a resampled black and white panchromatic image. This input is then degraded and resampled with a so-called polychromatic system optical transfer function (OTF), which is a weighted average of the OTFs over the spectral bandpass. In reality, a physical OTF is spectrally dependent, exhibiting varying structure with spatial frequency (especially in the presence of optical aberrations or sub-aperture phase errors). Given this spectral variation with spatial frequency, there is some concern the traditional gray world resampling approach may not address significant features of the image quality associated with sparse aperture systems. This research investigates the subject of how the image quality of a sparse aperture system varies with respect to a conventional telescope from a spectra-radiometric perspective, with emphasis on whether the restored sparse aperture image will be beset by spectral artifacts

Elastic Ladar Modeling for Synthetic Imaging Applications

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is a synthetic imagery generation model developed at the Center for Imaging Science (CIS) at the Rochester Institute of Technology (RIT). It is a quantitative first principle based model that calculates the sensor reaching radiance from the visible through to the long wave infrared on a spectral basis. DIRSIG generates a very accurate representation of what a sensor would see by modeling all the processes involved in the imaging chain. Currently, DIRSIG only models passive sources such as the sun and blackbody radiation due to the temperature of an object. Active systems have the benefit of the user being able to control the illumination source and tailor it for specific applications. Remote sensing Laser Detection and Ranging (LADAR) systems that utilize a laser as the active source have been in existence for over 30 years. Recent advances in tunable lasers and infrared detectors have allowed much more sophisticated and accurate work to be done, but a comprehensive spectral LADAR model has yet to be developed. In order to provide a tool to assist in LADAR development, this research incorporates a first principle based elastic LADAR model into DIRSIG. It calculates the irradiance onto the focal plane on a spectral basis for both the atmospheric and topographic return, based on the system characteristics and the assumed atmosphere. The geometrical form factor, a measure of the overlap between the sensor and receiver field-of-view, is carefully accounted for in both the monostatic and bistatic cases. The model includes the effect of multiple bounces from topographical targets. Currently, only direct detection systems will be modeled. Several sources of noise are extensively modeled, such as speckle from rough surfaces. Additionally, atmospheric turbulence effects including scintillation, beam effects, and image effects are accounted for. To allow for future growth, the model and coding are modular and anticipate the inclusion of advanced sensor models and inelastic scattering

Using Multispectral Information to Decrease the Spectral Artifacts in Sparse-Aperture Imagery

Optical sparse-aperture telescopes represent a promising new technology to increase the effective diameter of an optical system while reducing its weight and stowable size. The sub-apertures of a sparse-aperture system are phased to synthesize a telescope system that has a larger effective aperture than any of the independentsub-apertures. Sparse-apertures have mostly been modeled to date using a gray-world approximation where the input is a grayscale image. The gray-world model makes use of a polychromatic optical transfer function (OTF) where the spectral OTFs are averaged to form a single OTF. This OTF is then convolved with the grayscale image to create the resultant sparse-aperture image. The model proposed here uses a spectral image-cube as the input to create a panchromatic or multispectral result. These outputs better approximate an actual system because there is a higher spectral fidelity present than a gray-world model. Unlike its Cassegrain counterpart that has a well behaved OTF, the majority of sparse-aperture OTFs have very oscillatory and attenuated natures. When a spectral sparse-aperture model is used, spectral artifacts become apparent when thephasing errors increase beyond a certain threshold. This threshold can be based in part on the type of phasing error (i.e. piston, tip/tilt, and the amount present in each sub-aperture), as well as the collection conditions, including configuration, signal-to-noise ratio (SNR), and fill factor.This research addresses whether integrating a restored multispectral sparse-aperture image into a panchromatic image will decrease the amount of spectral artifacts present. The restored panchromatic image created from integrating multispectral images is compared to a conventional panchromatic sparse-aperture image. Conclusionsare drawn through image quality analysis and the change in spectral artifacts

Time-Gated Topographic LIDAR Scene Simulation

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model has been developed at the RochesterInstitute of Technology (RIT) for over a decade. The model is an established, first-principles based scene simulationtool that has been focused on passive multi- and hyper-spectral sensing from the visible to long wave infrared (0.4 to 14 µm). Leveraging photon mapping techniques utilized by the computer graphics community, a first-principles based elastic Light Detection and Ranging (LIDAR) model was incorporated into the passive radiometry framework so that the model calculates arbitrary, time-gated radiances reaching the sensor for both the atmospheric and topographicreturns. The active LIDAR module handles a wide variety of complicated scene geometries, a diverse set of surface and participating media optical characteristics, multiple bounce and multiple scattering effects, and a flexible suite of sensormodels. This paper will present the numerical approaches employed to predict sensor reaching radiances andcomparisons with analytically predicted results. Representative data sets generated by the DIRSIG model for a topographical LIDAR will be shown. Additionally, the results from phenomenological case studies including standard terrain topography, forest canopy penetration, and camouflaged hard targets will be presented

Incorporation of Polarization Into the DIRSIG Synthetic Image Generation Model

The Digital Imaging and Remote Sensing Synthetic Image Generation (DIRSIG) model uses a quantitative first principles approach to generate synthetic hyperspectral imagery. This paper presents the methods used to add modeling of polarization phenomenology. The radiative transfer equations were modified to use Stokes vectors for the radiance values and Mueller matrices for the energy-matter interactions. The use of Stokes vectors enables a full polarimetric characterization of the illumination and sensor reaching radiances. The bi-directional reflectance distribution function (BRDF) module was rewritten and modularized to accommodate a variety of polarized and unpolarized BRDF models. Two new BRDF models based on Torrance- Sparrow and Beard-Maxwell were added to provide polarized BRDF estimations. The sensor polarization characteristics are modeled using Mueller matrix transformations on a per pixel basis. All polarized radiative transfer calculations are performed spectrally to preserve the hyperspectral capabilities of DIRSIG. Integration over sensor bandpasses is handled by the sensor module

Hybridization of Hyperspectral Imaging Target Detection Algorithm Chains

Detection of a known target in an image can be accomplished using several different approaches. The complexity and number of steps involved in the target detection process makes a comparison of the different possible algorithm chains desirable. Of the different steps involved, some have a more significant impact than others on the final result - the ability to find a target in an image. These more important steps often include atmospheric compensation, noise and dimensionality reduction, background characterization, and detection (matched filtering for this research). A brief overview of the algorithms to be compared for each step will be presented. This research seeks to identify the most effective set of algorithms for a particular image or target type. Several different algorithms for each step will be presented, to include ELM, FLAASH, MNF, PPI, MAXD, the structured background matched filters OSP, and ASD. The chains generated by these algorithms will be compared using the Forest Radiance I HYDICE data set. Finally, receiver operating characteristic (ROC) curves will be calculated for each algorithm chain and, as an end result, a comparison of the various algorithm chains will be presented

Gas Plume Species Identification by Regression Analyses

Identification of constituent gases in effluent plumes is performed using linear least-squares regression techniques. Overhead thermal hyperspectral imagery is used for this study. Synthetic imagery is employed as the test-case for algorithm development. Synthetic images are generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model. The use of synthetic data provides a direct measure of the success of the algorithm through the comparison with truth map outputs. In image test-cases, plumes emanating from factory stacks will have been identified using a separate detection algorithm. The gas identification algorithm being developed in this work will then be used only on pixels having been determined to contain the plume. Stepwise linear regression is considered in this study. Stepwise regression is attractive for this application as only those gases truly in the plume will be present in the final model. Preliminary results from the study show that stepwise regression is successful at correctly identifying the gases present in a plume. Analysis of the results indicates that the spectral overlap of absorption features in different gas species leads to false identifications

Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters can be used to locate spectral targets by modeling scene background as either structured (geometric) with a set of endmembers (basis vectors) or as unstructured (stochastic) with a covariance or correlation matrix. These matrices are often calculated using all available pixels in a data set. In unstructured background research, various techniques for improving upon scene-wide methods have been developed, each involving either the removal of target signatures from the background model or the segmentation of image data into spatial or spectral subsets. Each of these methods increase the detection signal-to-background ratio (SBR) and the multivariate normality (MVN) of the data from which background statistics are calculated, thus increasing separation between target and non-target species in the detection statistic and ultimately improving thresholded target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This paper provides a review and comparison of methods in target exclusion, spatial subsetting and spectral pre-clustering, and introduces a new technique which combines these methods. The analysis provides insight into the merit of employing unstructured background characterization techniques, as well as limitations for their practical application