Credible Set Estimation, Analysis, and Applications in Synthetic Aperture Radar Canonical Feature Extraction

Traditional estimation schemes such as Maximum A Posterior (MAP) or Maximum Likelihood Estimation (MLE) determine the most likely parameter set associated with received signal data. However, traditional schemes do not retain entire posterior distribution, provide no confidence information associated with the final solution, and often rely on simple sampling methods which induce significant errors. Also, traditional schemes perform inadequately when applied to complex signals which often result in multi-modal parameter sets. Credible Set Estimation (CSE) provides a powerful and flexible alternative to traditional estimation schemes. CSE provides an estimation solution that accurately computes posterior distributions, retains confidence information, and provides a complete set of credible solutions. Determination of a credible region becomes especially important in Synthetic Aperture Radar (SAR) Automated Target Recognition (ATR) problems where signal complexity leads to multiple potential parameter sets. The presented research provides validation of methods for CSE, extension to high dimension/large observation sets, incorporation of Bayesian methods with previous work on SAR canonical feature extraction, and evaluation of the CSE algorithm. The results in this thesis show that: the CSE implementation of Gaussian-Quadrature techniques reduces computational error of the posterior distribution by up to twelve orders of magnitude, the presented formula for computation of the posterior distribution enables numerical evaluation for large observation sets (greater than 7,300 observations), and the algorithm is capable of producing M-th dimensional parameter estimates when applied to SAR canonical features. As such, CSE provides an ideal estimation scheme for radar, communications and other statistical problems where retaining the entire posterior distribution and associated confidence intervals is desirable

Improved Dictionary Formation and Search for Synthetic Aperture Radar Canonical Shape Feature Extraction

ATR requires detecting and estimating distinguishing characteristics of a target of interest. Radar data provides range and amplitude information; range distinguishes location relative to the radar whereas amplitude determines strength of reflectivity. Strong reflecting scattering features of targets are detected from a combination of radar returns, or radar PH data. Strong scatterers are modeled as canonical shapes (a plate, dihedral, trihedral, sphere, cylinder, or top-hat). Modeling the scatterers as canonical shapes takes the high dimensional radar PH from each scatterer and parameterizes the scatterer according to its location, size, and orientation. This thesis e ciently estimates the parameters of canonical shapes from radar PH data using dictionary search. Target scattering peaks are detected using 2-D SAR imaging. The parameters are estimated with decreased computation and improved accuracy relative to previous algorithms through reduced SAR image processing, informed parameter subspace bounding, and more e cient dictionary clustering. The effects of the collection fight path and radar parameters are investigated to permit pre-collection error analysis. The results show that even for a limited collection geometry, the dictionary estimates the canonical shape scatterer parameters well

Classification of Radar Targets Using Invariant Features

Automatic target recognition ATR using radar commonly relies on modeling a target as a collection of point scattering centers, Features extracted from these scattering centers for input to a target classifier may be constructed that are invariant to translation and rotation, i.e., they are independent of the position and aspect angle of the target in the radar scene. Here an iterative approach for building effective scattering center models is developed, and the shape space of these models is investigated. Experimental results are obtained for three-dimensional scattering centers compressed to nineteen-dimensional feature sets, each consisting of the singular values of the matrix of scattering center locations augmented with the singular values of its second and third order monomial expansions. These feature sets are invariant to translation and rotation and permit the comparison of targets modeled by different numbers of scattering centers. A metric distance metric is used that effectively identifies targets under real world conditions that include noise and obscuration

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Proceedings of the Augmented VIsual Display (AVID) Research Workshop

The papers, abstracts, and presentations were presented at a three day workshop focused on sensor modeling and simulation, and image enhancement, processing, and fusion. The technical sessions emphasized how sensor technology can be used to create visual imagery adequate for aircraft control and operations. Participants from industry, government, and academic laboratories contributed to panels on Sensor Systems, Sensor Modeling, Sensor Fusion, Image Processing (Computer and Human Vision), and Image Evaluation and Metrics

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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Phase History Decomposition for Efficient Scatterer Classification in SAR Imagery

A new theory and algorithm for scatterer classification in SAR imagery is presented. The automated classification process is operationally efficient compared to existing image segmentation methods requiring human supervision. The algorithm reconstructs coarse resolution subimages from subdomains of the SAR phase history. It analyzes local peaks in the subimages to determine locations and geometric shapes of scatterers in the scene. Scatterer locations are indicated by the presence of a stable peak in all subimages for a given subaperture, while scatterer shapes are indicated by changes in pixel intensity. A new multi-peak model is developed from physical models of electromagnetic scattering to predict how pixel intensities behave for different scatterer shapes. The algorithm uses a least squares classifier to match observed pixel behavior to the model. Classification accuracy improves with increasing fractional bandwidth and is subject to the high-frequency and wide-aperture approximations of the multi-peak model. For superior computational efficiency, an integrated fast SAR imaging technique is developed to combine the coarse resolution subimages into a final SAR image having fine resolution. Finally, classification results are overlaid on the SAR image so that analysts can deduce the significance of the scatterer shape information within the image context

Interferometric Synthetic Aperture RADAR and Radargrammetry towards the Categorization of Building Changes

The purpose of this work is the investigation of SAR techniques relying on multi image acquisition for fully automatic and rapid change detection analysis at building level. In particular, the benefits and limitations of a complementary use of two specific SAR techniques, InSAR and radargrammetry, in an emergency context are examined in term of quickness, globality and accuracy. The analysis is performed using spaceborne SAR data