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Volumetric synthetic aperture radar (VolSAR) analysis techniques and image formation algorithms suitable for short ranges and large scenes are presented. From a diffractive wave field inversion perspective, ultrawide beamwidth and near range SAR imaging scenarios can be viewed as a form of in situ SAR. Novel Huygens–Fresnel processing methods are introduced that empower in situ volumetric imaging with 2-D and 3-D aperture synthesis. These methods support coherent fusion across multiple separated frequency bands and also support spatial subaperture fusion of data from sparse sensor swarms. A novel signal analysis tool we call a chirp couplet is developed and shown to be useful in the expression and exploitation of temporal and spatial SAR chirp signals. Chirp couplets provide a physically motivated and unifying tool for both temporal and spatial elements in SAR signal analysis. In the temporal domain, chirp couplets provide a generalized formulation of the coupling of time and frequency that exists, for example, in linear frequency modulated waveforms. In the spatial domain, chirp couplets describe the coupling of sensor position and sensed wavenumber. Formulations of chirp couplets suitable for ray tomographic SAR algorithms (i.e., polar format algorithms) are contrasted with chirp couplets capable of supporting diffractive tomographic SAR algorithms (i.e., Stolt format algorithms). Scenarios in which diffraction limited resolution can be achieved with in situ VolSAR and finite length synthetic apertures are explored. VolSAR imaging methods are shown to escape the approximations that constrain the application and performance of range-Doppler and polar format methods. [ABSTRACT FROM AUTHOR]

Oil spills are one of the most dangerous catastrophes that threaten the oceans. Therefore, detecting and monitoring oil spills by means of remote sensing techniques that provide large-scale assessments is of critical importance to predict, prevent, and clean oil contamination. In this study, the detection of an oil spill using synthetic aperture radar (SAR) imagery is considered. Detection of the oil spill is performed using change detection algorithms between imagery acquired at different times. The specific algorithms used are the correlation coefficient change statistic and the intensity ratio change statistic algorithms. These algorithms and the probabilistic selection of threshold criteria are reviewed and discussed. A recently offered change detection method that depends on generating change maps of two images in a temporal sequence is used. An initial change map is obtained by cumulatively adding sequences in such a manner that common change areas are excluded and uncommon change areas are included. A final change map is obtained by comparing the first and the last images in the temporal sequence. This method requires at least three images to be employed and can be generalized to longer temporal image sequences. The purpose of this approach is to provide a double-check mechanism to the conventional approach and, thus, reduce the probability of false alarm while enhancing change detection. The algorithms are tested on 2010 Gulf of Mexico oil spill imagery. It is shown that the intensity ratio change statistic is a better tool for identification of the changes due to the oil spill compared to the correlation coefficient change statistic. It is also shown that the proposed method can reduce the probability of false alarm. [ABSTRACT FROM PUBLISHER]

Fourier analysis-based focusing of synthetic aperture radar (SAR) data collected during circular flight path is a recent advancement in SAR signal processing. This paper uses the House-holder transform to obtain a ground plane circular SAR (CSAR) signal phase history from the slant plane CSAR phase history by inverting the linear shift-varying system model, thereby circumventing the need for explicitly computing a pseudo-inverse. The Householder transform has recently been shown to have improved error bounds and stability as an underdetermined and ill-conditioned system solver, and the Householder transform is computationally efficient. [ABSTRACT FROM AUTHOR]

Image-driven data mining methods are described for image content segmentation, classification, and attribution, where each pixel location of an image-under-analysis is the center point of a pixel-block query that returns an estimated class label. Feature attribute estimates may also be mined when sufficient attribute strata exist in the data warehouse. Novel methods are presented for pixel-block mining, pattern similarity scoring, class label assignments, and attribute mining. These methods are based on a direct sum tree structure called a σ-tree that is utilized with near-neighbor similarity scoring. The σ-tree structure provides a solution to the challenge of high computation/memory costs of pixel-block similarity searching. The σ-trees are integrated into warehouse subsystems that provide referential capability into feature attribute data, resulting in a foundation for data mining called Source Optimized, Labeled, DIgital Expanded Representations (SOLDIER). The variable depth "bit-plane" data representations produced by σ-tree path selections provide an approach to image content segmentation, and provide a structure for formulation of Bayesian classification with data-adaptive Parzen classifiers with variably sized windows. Preliminary methods and results for postprocessing of mined feature-thematic layers for higher level scene understanding are also presented. Sample results are shown with synthetic aperture radar images and with high-resolution pan-sharpened satellite images of the Payagala, Sri Lanka area before the site was devastated by the 2004 Asian Tsunami. [ABSTRACT FROM AUTHOR]

Detection, classification, and attribution of high- resolution satellite image features in nearshore areas in the aftermath of Hurricane Katrina in Gulfport, MS, are investigated for damage assessments and emergency response planning. A system-level approach based on image-driven data mining with σ-tree structures is demonstrated and evaluated. Results show a capability to detect hurricane debris fields and storm-impacted nearshore features (such as wind-damaged buildings, sand de- posits, standing water, etc.) and an ability to detect and classify nonimpacted features (such as buildings, vegetation, roadways, railways, etc.). The σ-tree-based image information mining capability is demonstrated to be useful in disaster response planning by detecting blocked access routes and autonomously discovering candidate rescue/recovery staging areas. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) image classification for late-season rural land-cover estimation is investigated. A novel tree-structured nearest neighbor-like classifier is applied to polarimetric SAR intensity image pixel blocks. The novel tree structure, called a or-tree, is generated by an ordered summation of unweighted template refinements. Computation and memory costs of a σ-tree classifier grow linearly. The reduced costs of or-tree classifiers are obtained with the tradeoff of a guarantee of nearest neighbor mappings. Causal-anticausal refinement-template design methods, combined with causal multiple-stage search engine structures, are shown to yield sequential search decisions that are acceptably near-neighbor mappings. The performance of a σ-tree classifier is demonstrated for rural land-cover estimation with detected polarimetric C-band AirSAR pixel data. Experiments are conducted on various polarization/pixel block size combinations to evaluate the relative utility of spatial-only, polarimetric-only, and combined spatial/polarimetric classifier inputs. [ABSTRACT FROM AUTHOR]

Presents a signal set construction technique that provides an efficient means for realizing multidimensional trellis-coded modulation (TCM). Reviews of definition and mathematical formulation of a sigma-tree geometric structures; Overview of sigma-tree TCM codes; Decoder structure.