Results 61 to 70 of about 5,464 (214)
Isotropization of Quaternion-Neural-Network-Based PolSAR Adaptive Land Classification in Poincare-Sphere Parameter Space [PDF]
, 2018 Quaternion neural networks (QNNs) achieve high accuracy in polarimetric synthetic aperture radar classification for various observation data by working in Poincare-sphere-parameter space.
24434 +7 more
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Ecological Research, Volume 40, Issue 2, Page 120-132, March 2025.Using airborne light ranging and light detection (LiDAR) data of the Phil‐LiDAR 1 project, we attempted to develop models to estimate the above‐ground biomass AGB of an old‐growth mangrove forest in the KII Ecopark, Panay Island, Philippines. The common allometric model method showed a large underestimation of AGB for plots with higher canopy heights ...
Mohammad Shamim Hasan Mandal +8 more
wiley +1 more source
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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HIERARCHICAL CLASSIFICATION OF POLARIMETRIC SAR IMAGE BASED ON STATISTICAL REGION MERGING [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 Segmentation and classification of polarimetric SAR (PolSAR) imagery are very important for interpretation of PolSAR data. This paper presents a new object-oriented classification method which is based on Statistical Region Merging (SRM) segmentation ...
F. Lang, J. Yang, L. Zhao, D. Li
doaj +1 more source
Earth and Space Science, Volume 12, Issue 1, January 2025.Abstract Because of the remote nature of permafrost, it is difficult to collect data over large geographic regions using ground surveys. Remote sensing enables us to study permafrost at high resolution and over large areas. The Arctic‐Boreal Vulnerability Experiment's Permafrost Dynamics Observatory (PDO) contains data about permafrost subsidence ...
Elizabeth Wig +10 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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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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Unsupervised classification of multilook polarimetric SAR data using spatially variant wishart mixture model with double constraints [PDF]
, 2018 This paper addresses the unsupervised classification problems for multilook Polarimetric synthetic aperture radar (PolSAR) images by proposing a patch-level spatially variant Wishart mixture model (SVWMM) with double constraints.
Fu, Kun +4 more
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Training Sample Selection Based on SAR Images Quality Evaluation With Multi‐Indicators Fusion
IET Signal Processing, Volume 2025, Issue 1, 2025.In recent years, with the development of artificial neural networks, efficiently training models for synthetic aperture radar (SAR) image classification tasks has garnered significant attention from researchers. Particularly when dealing with datasets containing a large number of redundant samples, the selection of training samples becomes crucial for ...
Pengcheng Wang +3 more
wiley +1 more source