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This deliverable presents a first draft of the concepts, and their demonstration and evaluation for Knowledge Discovery in Databases (KDD). The Knowledge Discovery components are the data sources composed of Earth Observation images, metadata and GIS information, the Data Model Generation involving the content extraction and context analysis of the data(More)
The paper dwells into the comparison of parametric and non-parametric complex-valued 2D signal analysis, focusing the attention on complex-valued sub-meter SAR image data. The work is based on the study carried out in [1][2], [3][4] and [5]. In [1][2] a parametric Gauss-Markov Random Field (GMRF) model has been proposed for texture analysis and in [3][4] a(More)
In high-resolution (HR) and very-high resolution (VHR) synthetic aperture radar (SAR) images, focus is now on the patch-oriented image categorization in contrast to the pixel-based classification in low-resolution SAR images. SAR image categorization requires the generation of a compact feature descriptor that accurately defines the content of the image(More)
—The advent of submeter-resolution synthetic aperture radar (SAR) images from satellites such as TerraSAR-X has given a new dimension to SAR image understanding. Even though emphasis is always on discovering automatic means of target characterization , visual exploration of targets and objects is the first step in many applications. While considering the(More)
In this paper we compare Gauss-Markov Random Field (GMRF) and 4-D Representation based Time Frequency Analysis (TFA) methods for the analysis of targets in complex valued high-resolution SAR data. This work is based on the work carried out in [1], [2] & [3], [4] and it is an extension of the work presented by authors in [5]. In [1], [2] a parametric(More)
Modeling of synthetic aperture radar (SAR) images has been an important topic of research since the inception of SAR satellites. Many theoretical and empirical models have been presented in literature to accurately model the amplitude SAR images. The method of parameters estimation of the probability density function (PDF) for selected models is another(More)
Many users of Earth observation SAR images are only interested in individually selected application tasks. Thus, instead of applying universal data analysis tools, SAR users often aim at interactively controlled feature determination based on object size, object motion, double bouncing, or occultation. In contrast to these typically geometry-driven(More)