Metrics to evaluate compressions algorithms for RAW SAR data

Modern synthetic aperture radar (SAR) systems have size, weight, power and cost (SWAP-C) limitations since platforms are becoming smaller, while SAR operating modes are becoming more complex. Due to the computational complexity of the SAR processing required for modern SAR systems, performing the processing on board the platform is not a feasible option. Thus, SAR systems are producing an ever-increasing volume of data that needs to be transmitted to a ground station for processing. Compression algorithms are utilised to reduce the data volume of the raw data. However, these algorithms can cause degradation and losses that may degrade the effectiveness of the SAR mission. This study addresses the lack of standardised quantitative performance metrics to objectively quantify the performance of SAR data-compression algorithms. Therefore, metrics were established in two different domains, namely the data domain and the image domain. The data-domain metrics are used to determine the performance of the quantisation and the associated losses or errors it induces in the raw data samples. The image-domain metrics evaluate the quality of the SAR image after SAR processing has been performed. In this study three well-known SAR compression algorithms were implemented and applied to three real SAR data sets that were obtained from a prototype airborne SAR system. The performance of these algorithms were evaluated using the proposed metrics. Important metrics in the data domain were found to be the compression ratio, the entropy, statistical parameters like the skewness and kurtosis to measure the deviation from the original distributions of the uncompressed data, and the dynamic range. The data histograms are an important visual representation of the effects of the compression algorithm on the data. An important error measure in the data domain is the signal-to-quantisation-noise ratio (SQNR), and the phase error for applications where phase information is required to produce the output. Important metrics in the image domain include the dynamic range, the impulse response function, the image contrast, as well as the error measure, signal-to-distortion-noise ratio (SDNR). The metrics suggested that all three algorithms performed well and are thus well suited for the compression of raw SAR data. The fast Fourier transform block adaptive quantiser (FFT-BAQ) algorithm had the overall best performance, but the analysis of the computational complexity of its compression steps, indicated that it is has the highest level of complexity compared to the other two algorithms. Since different levels of degradation are acceptable for different SAR applications, a trade-off can be made between the data reduction and the degradation caused by the algorithm. Due to SWAP-C limitations, there also remains a trade-off between the performance and the computational complexity of the compression algorithm.Dissertation (MEng)--University of Pretoria, 2019.Electrical, Electronic and Computer EngineeringMEngUnrestricte

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Multiscale Representations for Manifold-Valued Data

We describe multiscale representations for data observed on equispaced grids and taking values in manifolds such as the sphere $S^2$, the special orthogonal group $SO(3)$, the positive definite matrices $SPD(n)$, and the Grassmann manifolds $G(n,k)$. The representations are based on the deployment of Deslauriers--Dubuc and average-interpolating pyramids "in the tangent plane" of such manifolds, using the $Exp$ and $Log$ maps of those manifolds. The representations provide "wavelet coefficients" which can be thresholded, quantized, and scaled in much the same way as traditional wavelet coefficients. Tasks such as compression, noise removal, contrast enhancement, and stochastic simulation are facilitated by this representation. The approach applies to general manifolds but is particularly suited to the manifolds we consider, i.e., Riemannian symmetric spaces, such as $S^{n-1}$, $SO(n)$, $G(n,k)$, where the $Exp$ and $Log$ maps are effectively computable. Applications to manifold-valued data sources of a geometric nature (motion, orientation, diffusion) seem particularly immediate. A software toolbox, SymmLab, can reproduce the results discussed in this paper

Detecting anomalies in remotely sensed hyperspectral signatures via wavelet transforms

An automated subpixel target detection system has been designed and tested for use with remotely sensed hyperspectral images. A database of hyperspectral signatures was created to test the system using a variety of Gaussian shaped targets. The signal-to-noise ratio of the targets varied from -95dB to -50dB. The system utilizes a wavelet-based method (discrete wavelet transform) to extract an energy feature vector from each input pixel signature. The dimensionality of the feature vector is reduced to a one-dimensional feature scalar through the process of linear discriminant analysis. Signature classification is determined by nearest mean criterion that is used to assign each input signature to one of two classes, no target present or target present. Classification accuracy ranged from nearly 60% with target SNR at -95dB without any a priori knowledge of the target, to 100% with target SNR at -50dB and a priori knowledge about the location of the target within the spectral bands of the signature

Hierarchical Fusion Based Deep Learning Framework for Lung Nodule Classification

Lung cancer is the leading cancer type that causes the mortality in both men and women. Computer aided detection (CAD) and diagnosis systems can play a very important role for helping the physicians in cancer treatments. This dissertation proposes a CAD framework that utilizes a hierarchical fusion based deep learning model for detection of nodules from the stacks of 2D images. In the proposed hierarchical approach, a decision is made at each level individually employing the decisions from the previous level. Further, individual decisions are computed for several perspectives of a volume of interest (VOI). This study explores three different approaches to obtain decisions in a hierarchical fashion. The first model utilizes the raw images. The second model uses a single type feature images having salient content. The last model employs multi-type feature images. All models learn the parameters by means of supervised learning. In addition, this dissertation proposes a new Trilateral Filter to extract salient content of 2D images. This new filter includes a second anisotropic Laplacian kernel in addition to the Bilateral filter’s range kernel. The proposed CAD frameworks are tested using lung CT scans from the LIDC/IDRI database. The experimental results showed that the proposed multi-perspective hierarchical fusion approach significantly improves the performance of the classification

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

Anomaly detection in hyperspectral signatures using automated derivative spectroscopy methods

The goal of this research was to detect anomalies in remotely sensed Hyperspectral images using automated derivative based methods. A database of Hyperspectral signatures was used that had simulated additive Gaussian anomalies that modeled a weakly concentrated aerosol in several spectral bands. The automated pattern detection system was carried out in four steps. They were: (1) feature extraction, (2) feature reduction through linear discriminant analysis, (3) performance characterization through receiver operating characteristic curves, and (4) signature classification using nearest mean and maximum likelihood classifiers. The Hyperspectral database contained signatures with various anomaly concentrations ranging from weakly present to moderately present and also anomalies in various spectral reflective and absorptive bands. It was found that the automated derivative based detection system gave classification accuracies of 97 percent for a Gaussian anomaly of SNR -45 dB and 70 percent for Gaussian anomaly of SNR -85 dB. This demonstrates the applicability of using derivative analysis methods for pattern detection and classification with remotely sensed Hyperspectral images

Synthetic aperture radar/LANDSAT MSS image registration

Algorithms and procedures necessary to merge aircraft synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) imagery were determined. The design of a SAR/LANDSAT data merging system was developed. Aircraft SAR images were registered to the corresponding LANDSAT MSS scenes and were the subject of experimental investigations. Results indicate that the registration of SAR imagery with LANDSAT MSS imagery is feasible from a technical viewpoint, and useful from an information-content viewpoint

Metrics to evaluate compressions algorithms for RAW SAR data

Modern synthetic aperture radar (SAR) systems have size, weight, power and cost (SWAP-C) limitations since platforms are becoming smaller, while SAR operating modes are becoming more complex. Due to the computational complexity of the SAR processing required for modern SAR systems, performing the processing on board the platform is not a feasible option. Thus, SAR systems are producing an ever-increasing volume of data that needs to be transmitted to a ground station for processing. Compression algorithms are utilised to reduce the data volume of the raw data. However, these algorithms can cause degradation and losses that may degrade the effectiveness of the SAR mission. This study addresses the lack of standardised quantitative performance metrics to objectively quantify the performance of SAR data-compression algorithms. Therefore, metrics were established in two different domains, namely the data domain and the image domain. The data-domain metrics are used to determine the performance of the quantisation and the associated losses or errors it induces in the raw data samples. The image-domain metrics evaluate the quality of the SAR image after SAR processing has been performed. In this study three well-known SAR compression algorithms were implemented and applied to three real SAR data sets that were obtained from a prototype airborne SAR system. The performance of these algorithms were evaluated using the proposed metrics. Important metrics in the data domain were found to be the compression ratio, the entropy, statistical parameters like the skewness and kurtosis to measure the deviation from the original distributions of the uncompressed data, and the dynamic range. The data histograms are an important visual representation of the effects of the compression algorithm on the data. An important error measure in the data domain is the signal-to-quantisation-noise ratio (SQNR), and the phase error for applications where phase information is required to produce the output. Important metrics in the image domain include the dynamic range, the impulse response function, the image contrast, as well as the error measure, signal-to-distortion-noise ratio (SDNR). The metrics suggested that all three algorithms performed well and are thus well suited for the compression of raw SAR data. The fast Fourier transform block adaptive quantiser (FFT-BAQ) algorithm had the overall best performance, but the analysis of the computational complexity of its compression steps, indicated that it is has the highest level of complexity compared to the other two algorithms. Since different levels of degradation are acceptable for different SAR applications, a trade-off can be made between the data reduction and the degradation caused by the algorithm. Due to SWAP-C limitations, there also remains a trade-off between the performance and the computational complexity of the compression algorithm.Dissertation (MEng)--University of Pretoria, 2019.TM2019Electrical, Electronic and Computer EngineeringMEngUnrestricte