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Understanding Synthetic Aperture Radar Images

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TLDR
In this paper, the principles of SAR image image formation are discussed and an analysis technique for multi-dimensional image analysis is presented based on RCS Reconstruction Filters and Texture Exploitation.
Abstract
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.

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First Polish SAR trials

TL;DR: In this article, the authors presented the first focused SAR image obtained in the following year by the Telecommunication Research Institute and the Warsaw University of Technology in order to work out efficient algorithms for radar imaging in different radar modes.
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InSAR Coherence Estimation for Small Data Sets and Its Impact on Temporal Decorrelation Extraction

TL;DR: A novel coherence estimation method for small data sets is presented for interferometric synthetic aperture radar (SAR) data processing and geoscience applications and can be more accurately detected compared to other conventional methods.
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An Automatic Ship Detection Method for PolSAR Data Based on K-Wishart Distribution

TL;DR: An automatic ship detection algorithm for PolSAR data, termed K-Wishart detector, which utilizes non-Gaussian K-wishart classifier and incorporates the polarimetric SPAN parameter to identify the ships is presented.
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Performance Analysis of Phase Gradient Autofocus for Compensating Ionospheric Phase Scintillation in BIOMASS P-Band SAR Data

TL;DR: Simulations based on scenes derived from PALSAR data demonstrate the effectiveness of PGA, which adds significantly to post-PGA degradation in the integrated and peak sidelobe ratios for large values of CkL.
Journal ArticleDOI

Unsupervised images segmentation via incremental dictionary learning based sparse representation

TL;DR: A novel Dictionary Learning and Sparse Representation-based Classifier for image segmentation and the superiority of the proposed method to its counterparts is proved.
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