Jagmal Singh

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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)
This letter presents synthetic aperture radar (SAR) image classification based on feature descriptors within the discrete wavelet transform (DWT) domain using parametric and nonparametric features. The DWT enables an efficient multiresolution description of SAR images due to its geometric and stochastic features. A 2-D DWT, a real 2-D oriented dual tree(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)
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)
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)
In contrast to these typically geometry-driven approaches, we propose a combined radiometry/morphology-driven approach that allows us to detect, identify and classify arbitrary and unexpected objects being “machine visible” in high resolution SAR images. Thus, we can look for any kind of generic object being characterized by specific brightness and shape(More)