Results 51 to 60 of about 840 (174)
A Box Gradient‐Based Anisotropic Diffusion Framework for Polarimetric SAR Despeckling
Electronics Letters, Volume 62, Issue 1, January/December 2026.This paper proposes a novel anisotropic diffusion framework for PolSAR despeckling, driven by a newly defined complex‐domain Box gradient. The method adaptively suppresses speckle while preserving structural and point‐target details, and experiments on real AIRSAR data confirm superior performance over existing filters.
Songli Han, Dawei Ren, Jian Yang
wiley +1 more source
IET Radar, Sonar &Navigation, Volume 20, Issue 1, January/December 2026.Polarimetric SAR data's inherent complex‐valued nature demands algorithms that work directly with complex representations, yet most deep‐learning approaches sidestep this by converting to the real domain. We implement and evaluate complex‐valued convolutional autoencoders that compress and accurately reconstruct full‐polarimetric SAR signals—preserving
Quentin Gabot +4 more
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Discussion on Application of Polarimetric Synthetic Aperture Radar in Marine Surveillance
Leida xuebao, 2016 Synthetic Aperture Radar (SAR), an important earth observation sensor, has been used in a wide range of applications for land and marine surveillance. Polarimetric SAR (PolSAR) can obtain abundant scattering information of a target to improve the ability
Zhang Jie +3 more
doaj +1 more source
A Novel Multi-Feature Joint Learning Method for Fast Polarimetric SAR Terrain Classification
IEEE Access, 2020 Polarimetric synthetic aperture radar (PolSAR) image classification is one of the most important study areas for PolSAR image processing. Many kinds of PolSAR features can be extracted for PolSAR image classification, such as the scattering, polarimetric
Junfei Shi, Haiyan Jin, Xiaohua Li
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Classification of PolSAR Images by Stacked Random Forests [PDF]
ISPRS International Journal of Geo-Information, 2018 This paper proposes the use of Stacked Random Forests (SRF) for the classification of Polarimetric Synthetic Aperture Radar images. SRF apply several Random Forest instances in a sequence where each individual uses the class estimate of its predecessor as an additional feature. To this aim, the internal node tests are designed to work not only directly
Ronny Hänsch, Olaf Hellwich
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IET Radar, Sonar &Navigation, Volume 20, Issue 1, January/December 2026.This paper presents a multistatic, frequency‐ and polarimetry‐adaptive autofocus algorithm (F‐Pol) for SAR imaging of moving targets undergoing complex, six‐degrees‐of‐freedom (6‐DoF) motion. Building on Localised Threshold Sharpness (LTS), the method exploits agreement across frequency sub‐bands and polarisation channels to improve pulse‐by‐pulse ...
Anmol Rattan, Daniel Andre, Mark Finnis
wiley +1 more source
Remote Sensing, 2022 Polarimetric synthetic aperture radar (PolSAR) image classification is a pixel-wise issue, which has become increasingly prevalent in recent years. As a variant of the Convolutional Neural Network (CNN), the Fully Convolutional Network (FCN), which is ...
Wen Xie, Licheng Jiao, Wenqiang Hua
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Applications of the Mellin Transform in Analyzing Survival Data With the Cubic Transmuted Class
Journal of Probability and Statistics, Volume 2026, Issue 1, 2026.The development of new models aims to provide probability distributions that can accurately describe unusual phenomena, such as those observed in survival analysis, which often pose challenges due to factors like time dependence and data censoring. Among the diverse classes and models available in the literature, there has been growing interest in ...
Marília Oliveira +3 more
wiley +1 more source
Robust Classification of PolSAR Images Based on Pinball loss Support Vector Machine
Leida xuebao, 2019 Given the problems that the amount of supervised information in the Polarimetric Synthetic Aperture Radar (PolSAR) image is low and the speckle noise is difficult to eliminate, in this study, a robust classification algorithm for PolSAR image based on ...
ZHANG Lamei +3 more
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Feature Selection for Edge Detection in PolSAR Images
Remote Sensing, 2023 Edge detection is one of the most critical operations for moving from data to information. Finding edges between objects is relevant for image understanding, classification, segmentation, and change detection, among other applications. The Gambini Algorithm is a good choice for finding evidence of edges.
Anderson A. De Borba +3 more
openaire +3 more sources