The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information systems, most commercial systems stop short of the actual integration of available data. Data fusion is the process of fusing multiple records representing the same real-world object into a single, consistent, and clean representation.This article places data fusion into the greater context of data integration, precisely defines the goals of data fusion, namely, complete, concise, and consistent data, and highlights the challenges of data fusion, namely, uncertain and conflicting data values. We give an overview and classification of different ways of fusing data and present several techniques based on standard and advanced operators of the relational algebra and SQL. Finally, the article features a comprehensive survey of data integration systems from academia and industry, showing if and how data fusion is performed in each.

Data fusion

Synthetic Aperture Radar (SAR) has been widely used for Earth remote sensing for more than 30 years. It provides high-resolution, day-and-night and weather-independent images for a multitude of applications ranging from geoscience and climate change research, environmental and Earth system monitoring, 2-D and 3-D mapping, change detection, 4-D mapping (space and time), security-related applications up to planetary exploration. With the advances in radar technology and geo/bio-physical parameter inversion modeling in the 90s, using data from several airborne and spaceborne systems, a paradigm shift occurred from the development driven by the technology push to the user demand pull. Today, more than 15 spaceborne SAR systems are being operated for innumerous applications. This paper provides first a tutorial about the SAR principles and theory, followed by an overview of established techniques like polarimetry, interferometry and differential interferometry as well as of emerging techniques (e.g., polarimetric SAR interferometry, tomography and holographic tomography). Several application examples including the associated parameter inversion modeling are provided for each case. The paper also describes innovative technologies and concepts like digital beamforming, Multiple-Input Multiple-Output (MIMO) and bi- and multi-static configurations which are suitable means to fulfill the increasing user requirements. The paper concludes with a vision for SAR remote sensing.

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A tutorial on synthetic aperture radar

Localized fluid flow measurements with an He-Ne laser spectrometer

We present a novel automatic and unsupervised change-detection approach specifically oriented to the analysis of multitemporal single-channel single-polarization synthetic aperture radar (SAR) images. This approach is based on a closed-loop process made up of three main steps: (1) a novel preprocessing based on a controlled adaptive iterative filtering; (2) a comparison between multitemporal images carried out according to a standard log-ratio operator; and (3) a novel approach to the automatic analysis of the log-ratio image for generating the change-detection map. The first step aims at reducing the speckle noise in a controlled way in order to maximize the discrimination capability between changed and unchanged classes. In the second step, the two filtered multitemporal images are compared to generate a log-ratio image that contains explicit information on changed areas. The third step produces the change-detection map according to a thresholding procedure based on a reformulation of the Kittler-Illingworth (KI) threshold selection criterion. In particular, the modified KI criterion is derived under the generalized Gaussian assumption for modeling the distributions of changed and unchanged classes. This parametric model was chosen because it is capable of better fitting the conditional densities of classes in the log-ratio image. In order to control the filtering step and, accordingly, the effects of the filtering process on change-detection accuracy, we propose to identify automatically the optimal number of despeckling filter iterations [Step 1] by analyzing the behavior of the modified KI criterion. This results in a completely automatic and self-consistent change-detection approach that avoids the use of empirical methods for the selection of the best number of filtering iterations. Experiments carried out on two sets of multitemporal images (characterized by different levels of speckle noise) acquired by the European Remote Sensing 2 satellite SAR sensor confirm the effectiveness of the proposed unsupervised approach, which results in change-detection accuracies very similar to those that can be achieved by a manual supervised thresholding.

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An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images

Interferometric radar techniques often necessitate two-dimensional (2-D) phase unwrapping, defined here as the estimation of unambiguous phase data from a 2-D array known only modulo 2pi rad. We develop a maximum a posteriori probability (MAP) estimation approach for this problem, and we derive an algorithm that approximately maximizes the conditional probability of its phase-unwrapped solution given observable quantities such as wrapped phase, image intensity, and interferogram coherence. Examining topographic and differential interferometry separately, we derive simple, working models for the joint statistics of the estimated and the observed signals. We use generalized, nonlinear cost functions to reflect these probability relationships, and we employ nonlinear network-flow techniques to approximate MAP solutions. We apply our algorithm both to a topographic interferogram exhibiting rough terrain and layover and to a differential interferogram measuring the deformation from a large earthquake. The MAP solutions are complete and are more accurate than those of other tested algorithms.

Two-dimensional phase unwrapping with use of statistical models for cost functions in nonlinear optimization

Introduction. Principles of SAR Image Formation. Image Defects and their Correction. Fundamental Properties of SAR Images. Data Models. RCS Reconstruction Filters. RCS Classification and Segmentation. Texture Exploitation. Correlated Textures. Information in Multi-Channel SAR. Analysis Techniques for Multi-Dimensional Images. Target Information. Image Classification.

Understanding Synthetic Aperture Radar Images

This paper analyses various estimators for characterizing synthetic aperture radar clutter textures. First, we consider maximum likelihood estimators, which require specific knowledge of the form of the probability distribution of the data but would be expected to yield the best performance. Both K- and Weibull-distributed clutter models, which are often applied to characterize natural SAR clutter, are considered. Though a full maximum likelihood solution is impossible for the K distribution, we derive an approximate one for the multi-look case. We next derive expressions for limiting errors in a variety of direct texture estimators and compare their predicted performance with the maximum likelihood estimates in a search for robust, optimum texture estimators.

https://iopscience.iop.org/article/10.1088/0022-3727/26/11/002/pdf

Optimum texture estimators for SAR clutter

Different window configurations and measures for detecting edges between regions of different mean in synthetic-aperture radar (SAR) images are compared. Two criteria for optimisation are considered: (i) maximising the total probability of detecting an edge within a window; and (ii) maximising the accuracy with which the edge position can be determined. Clearly, an ideal edge detector would combine both properties. Maximum-likelihood solutions to the two criteria are discussed and it is shown how they determine the choice of both window configuration and the measure adopted. The performance degradation encountered when the tests are applied to the scenario for which they are not optimised is compared and the test which best meets both original criteria is identified. Combination of the good features of each to yield the best overall performance in a two-stage edge-detection scheme is discussed.

Optimum edge detection in SAR

The independent-scatterer K -distribution model, which has been introduced to describe a variety of scattering situations, is extended to include the effects of correlation between scatterers and finite illumination size. A model in which the array of scatterers is represented by a Γ -lorentzian cross-section fluctuation is proposed. In the appropriate limits this reduces to the independent K -distribution model. Following scattering by the Γ -lorentzian surface, the autocorrelation function and moments of the detected intensity for radiation of arbitrary beamwidth and wavefront curvature are derived. The results are compared with the predictions of the independent K -distribution model and the implications of the differences, which reflect the fact that the independent model cannot represent spatial averaging over the correlations within the surface, are discussed.

A Model for Non-Rayleigh Scattering Statistics

Previously the production of focused, undistorted, synthetic-aperture radar (SAR) images in a routine way has been described. The means by which information about the scene can be extracted from the resultant images is discussed. The importance of prior knowledge about the form of the scene for interpreting the image data is shown. Different types of model are introduced and their implications for information extraction are examined.

http://iopscience.iop.org/article/10.1088/0022-3727/24/9/001/pdf

Christopher John Oliver

Papers

Understanding Synthetic Aperture Radar Images

Optimum texture estimators for SAR clutter

Optimum edge detection in SAR

A Model for Non-Rayleigh Scattering Statistics

Information from SAR images