Binary Classification of an Unknown Object through Atmospheric Turbulence Using a Polarimetric Blind-Deconvolution Algorithm Augmented with Adaptive Degree of Linear Polarization Priors

This research develops an enhanced material-classification algorithm to discriminate between metals and dielectrics using passive polarimetric imagery degraded by atmospheric turbulence. To improve the performance of the existing technique for near-normal collection geometries, the proposed algorithm adaptively updates the degree of linear polarization (DoLP) priors as more information becomes available about the scene. Three adaptive approaches are presented. The higher-order super-Gaussian method fits the distribution of DoLP estimates with a sum of two super-Gaussian functions to update the priors. The Gaussian method computes the classification threshold value, from which the priors are updated, by fitting the distribution of DoLP estimates with a sum of two Gaussian functions. Lastly, the distribution-averaging method approximates the threshold value by finding the mean of the DoLP distribution. The experimental results confirm that the new adaptive method significantly extends the collection geometry range of validity for the existing technique

Three Channel Polarimetric Based Data Deconvolution

A three channel polarimetric deconvolution algorithm was developed to mitigate the degrading effects of atmospheric turbulence in astronomical imagery. Tests were executed using both simulation and laboratory data. The resulting efficacy of the three channel algorithm was compared to a recently developed two channel approach under identical conditions ensuring a fair comparison amongst both algorithms. Two types of simulations were performed. The first was a binary star simulation to compare resulting resolutions between the three and two channel algorithms. The second simulation measured how effective both algorithms could deconvolve a blurred satellite image. The simulation environment assumed the key parameters of Fried\u27s Seeing parameter, , and telescope lens diameters of and . The simulation results showed that the three channel algorithm always reconstructed the true image as good as or better than the two channel approach, while the total squared error was always significantly better for the three channel algorithm. The next step is comparing the two algorithms in the laboratory environment. However, the laboratory imagery was not actually blurred by atmospheric turbulence, but instead camera defocusing was used to simulate the blurring that would be caused by atmospheric turbulence. The results show that the three channel significantly outperforms the two channel in a visual reconstruction of the true image

Material Characterization Using Passive Multispectral Polarimetric Imagery

A new method for characterization of unknown targets using passive multispectral polarimetric imagery is presented. Previous work makes use of a pBRDF derived equation for the degree of linear polarization and with the aid of measurements at multiple incident angles estimates refractive index and reflection angle. This work uses known incident and reflection angles along with dispersion equations and polarimetric data at multiple wavelengths to recover the index of refraction. Although imagery is collected with a division of time polarimeter and a spectral filter wheel in iterative, manual steps, the new algorithm could be applied to any set of registered multispectral polarimetric images most notably those produced by a recently introduced division of focal plane multispectral polarimetric sensor. Experimental results are presented showing the novel algorithm\u27s ability to classify and characterize a range of materials

Multichannel Blind Deconvolution of Circularly Polarized Imagery

Current methods can be used to recreate an expected Stokes polarization vector from measured light for characterization purposes. However, this only applies to three of the Stokes parameters, that is, the linear polarization components. Circular polarization is not currently being utilized for object characterization because the mathematical complexity is greater than for that of linear polarization. This research will analyze a way to expand existing algorithms to include circular polarization and enable the complete reconstruction of the Stokes vector from the measured light into a blind deconvolution algorithm for Stokes estimation and image reconstruction

Analytical modeling, performance analysis, and optimization of polarimetric imaging system

Polarized light can provide additional information about a scene that cannot be obtained directly from intensity or spectral images. Rather than treating the optical field as scalar, polarization images seek to obtain the vector nature of the optical field from the scene. Polarimetry thus has been found to be useful in several applications, including material classification and target detection. Recently, optical polarization has been identified as an emerging technique and has shown promising applications in passive remote sensing. Compared with the traditional spectral content of the scene, polarimetric signatures are much more dependent on the scene geometry and the polarimetric bidirectional reflectance distribution function (pBRDF) of the objects. Passive polarimetric scene simulation has been shown to be helpful in better understanding such phenomenology. However, the combined effects of the scene characteristics, the sensor noise and optical imperfections, and the different processing algorithm implementations on the overall system performance have not been systematically studied. To better understand the effects of various system attributes and help optimize the design and use of polarimetric imaging system, an analytical model has been developed to predict the system performance. A detailed introduction of the analytical model is first presented. The model propagates the first and second order statistics of radiance from a scene model to a sensor model, and finally to a processing model. Validation with data collected from a division of time polarimeter show good agreement between model predictions and measurements. It has been shown that the analytical model is able to predict the general polarization behavior and data trends with different scene geometries. Based on the analytical model we then define several system performance metrics to evaluate the polarimetic signatures of different objects as well as target detection performance. Parameter tradeoff studies have been conducted for analysis of potential system performance. Finally based on the analytical model and system performance metrics we investigate optimal filter configurations to sense polarization. We develop an adaptive polarimetric target detector to determine the optimum analyzer orientations for a multichannel polarization-sensitive optical system. Compared with several conventional operation methods, we find that better target detection performance is achieved with our algorithm

Air Force Institute of Technology Research Report 2011

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics

Polarimetric Image Reconstruction Algorithms.

In the field of imaging polarimetry Stokes parameters are sought and must be inferred from noisy and blurred intensity measurements. Using a penalized-likelihood estimation framework we investigate reconstruction quality when estimating intensity images and then transforming to Stokes parameters (traditional estimator), and when estimating Stokes parameters directly (Stokes estimator). We define our cost function for reconstruction by a weighted least squares data fit term and a regularization penalty. It is shown that under quadratic regularization, the traditional and Stokes estimators can be made equal by appropriate choice of regularization parameters. It is empirically shown that, when using edge preserving regularization, estimating the Stokes parameters directly leads to lower textsc{RMS} error in reconstruction. Also, the addition of a cross channel regularization term further lowers the textsc{RMS} error for both methods especially in the case of low textsc{SNR}. The technique of phase diversity has been used in traditional incoherent imaging systems to jointly estimate an object and optical system aberrations. We extend the technique of phase diversity to polarimetric imaging systems. Specifically, we describe penalized-likelihood methods for jointly estimating Stokes images and optical system aberrations from measurements that contain phase diversity. Jointly estimating Stokes images and optical system aberrations involves a large parameter space. A closed-form expression for the estimate of the Stokes images in terms of the aberration parameters is derived and used in a formulation that reduces the dimensionality of the search space to the number of aberration parameters only. We compare the performance of the joint estimator under both quadratic and edge-preserving regularization. The joint estimator with edge-preserving regularization yields higher fidelity polarization estimates than with quadratic regularization. Under quadratic regularization, using the reduced-parameter search strategy, accurate aberration estimates can be obtained without recourse to regularization ``tuning''. Phase-diverse wavefront sensing is emerging as a viable candidate wavefront sensor for adaptive-optics systems. In a quadratically penalized weighted least squares estimation framework a closed form expression for the object being imaged in terms of the aberrations in the system is available.Ph.D.Applied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77925/1/jvalenz_1.pd

Passively Estimating Index of Refraction for Specular Reflectors Using Polarimetric Hyperspectral Imaging

As off-nadir viewing platforms becoming increasingly prevalent in remote sensing, material classification and ID techniques robust to changing viewing geometries must be developed. Traditionally, either reflectivity or emissivity are used for classification, but these quantities vary with viewing angle. Instead, estimating index of refraction may be advantageous as it is invariant with respect to viewing geometry. This work focuses on estimating index of refraction from LWIR (875-1250 wavenumbers) polarimetric hyperspectral radiance measurements

Air Force Institute of Technology Research Report 2020

This Research Report presents the FY20 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document