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Householder transform is employed for obtaining a ground plane circular synthetic aperture radar (CSAR) signal phase history. The approach is shown to highly improve the error bounds and stability of the process.

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 [sigma]-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 deposits, standing water, etc.) and an ability to detect and classify nonimpacted features (such as buildings, vegetation, roadways, railways, etc.). The [sigma]-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. Index Terms--Emergency response planning, image-driven data mining, image information mining, satellite image hurricane disaster assessments, or-tree classifiers.

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 [sigma]-tree, is generated by an ordered summation of unweighted template refinements. Computation and memory costs of a [sigma]-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 [sigma]-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-ouly, polarimetric-ouly, and combined spatial/polarimetric classifier inputs. Index Terms--Additive successive refinements, direct sum, residual vector quantization, successive approximation, synthetic aperture radar (SAR) land-use classification, [sigma]-trees.

A survey of the advances in design techniques for residual vector quantization (RVQ) and encoder and decoder codebooks is given. A residual quantizer based on the scalar version of RVQ performs well in generalized embedded wavelet transform coding systems. The encoder has a variable block rate coder and a rate control mechanism. The control mechanism is similar to the rate-distortion bound analyses of Gaussian sources with memory.

Necessary conditions for the optimality of variable-rate residual vector quantizers are derived, and an iterative descent algorithm based on a Lagrangian formulation is introduced for designing residual vector quantizers having minimum average distortion subject to an entropy constraint. Simulation results for entropy-constrained residual vector quantizers are presented for memoryless Gaussian, Laplacian, and uniform sources. A Gauss-Markov source is also considered. The rate-distortion performance is shown to be competitive with that of entropy-constrained vector quantization and entropy-constrained trellis-coded quantization. Index Terms - Vector quantization, residual vector quantization, entropy, source coding, and rate-distortion function.

The entropy-constrained residual vector quantization design algorithm is a suitable technique in designing codebooks for image coding. The technique is superior to entropy-constrained vector quantization and JPEG in terms of rate-distortion characteristics, memory and computation necessities. Suitable image reproduction quality can be obtained at low bit rates.

The use of direct sum codebooks to minimize the memory requirements of vector quantizers is investigated. Assuming arbitrary fixed partitions, necessary conditions for minimum distortion codebooks are derived: first for scalar codebooks, assuming mean-squared error distortion, and then for vector codebooks and a broader class of distortion measures. An iterative procedure is described for designing locally optimal direct sum codebooks. Both optimal and computationally efficient suboptimal encoding schemes are considered. It is shown that although an optimal encoding can be implemented by a sequential encoder, the complexity of implementing optimal stagewise partitions generally exceeds the complexity of an exhaustive search of the direct sum codebook. It is also shown that sequential nearest-neighbor encoders, which encode each stagewise residual with the nearest-neighbor code vector from the associated stagewise codebook, can be extremely inefficient. This is particularly true for direct sum quantizers with high output rates or with many stages. The M-search method is explored as one method of improving the effectiveness of suboptimal sequential encoders. Representative results of simulated direct sum quantizers are presented for Laplacian, Gaussian and Gauss-Markov sources.

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]

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.