Results 61 to 70 of about 7,181 (241)
Imaging ionospheric inhomogeneities using spaceborne synthetic aperture radar [PDF]
, 2011 We present a technique and results of 2-D imaging of Faraday rotation and total electron content using spaceborne L band polarimetric synthetic aperture radar (PolSAR).
Chapman, B. +4 more
core +2 more sources
DNN-Based PolSAR Image Classification on Noisy Labels
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Deep neural networks (DNNs) appear to be a solution for the classification of polarimetric synthetic aperture radar (PolSAR) data in that they outperform classical supervised classifiers under the condition of sufficient training samples. The design of a classifier is challenging because DNNs can easily overfit due to limited remote sensing training ...
Jun Ni +5 more
openaire +3 more sources
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
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
core +2 more sources
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
Image fusion techniqes for remote sensing applications [PDF]
, 2002 Image fusion refers to the acquisition, processing and synergistic combination of information provided by various sensors or by the same sensor in many measuring contexts.
Bruzzone, Lorenzo +4 more
core +1 more source
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
A Non-Parametric Texture Descriptor for Polarimetric SAR Data with Applications to Supervised Classification [PDF]
, 2014 The paper describes a novel representation of polarimetric SAR (PolSAR) data that is inherently non-parametric and therefore particularly suited for characterising data in which the commonly adopted hypothesis of Gaussian backscatter is not ...
Jäger, Marc, Reigber, Andreas
core
Offshore Metallic Platforms Observation Using Dual-Polarimetric TS-X/TD-X Satellite Imagery: A Case Study in the Gulf of Mexico [PDF]
, 2017 Satellite-based synthetic aperture radar (SAR) has been proven to be an effective tool for ship monitoring. Offshore platforms monitoring is a key topic for both safety and security of the maritime domain.
Marino, Armando +2 more
core +3 more sources
Consistency Regularization Semisupervised Learning for PolSAR Image Classification
International Journal of Intelligent Systems, Volume 2025, Issue 1, 2025.Polarimetric Synthetic Aperture Radar (PolSAR) images have emerged as an important data source for land cover classification research due to their all‐weather, all‐day monitoring capabilities. Deep learning‐based classification methods have recently gained significant attention in PolSAR image classification since they have demonstrated excellent ...
Yu Wang +3 more
wiley +1 more source