Results 51 to 60 of about 2,377 (186)
, 2015 We propose a new method for PolSAR (Polarimetric Synthetic Aperture Radar) imagery classification based on stochastic distances in the space of random matrices obeying complex Wishart distributions.
Alvarez, Luis +3 more
core +1 more source
Polarimetric SAR cross-calibration method based on stable distributed targets
Geo-spatial Information SciencePolarimetric calibration is essential for the pre-processing of Polarimetric Synthetic Aperture Radar (PolSAR) data because it effectively mitigates polarimetric distortions in the measured PolSAR data.
Yongsheng Zhou +5 more
doaj +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
Error Source Analysis and Correction of GF-3 Polarimetric Data
Remote Sensing, 2018 The GaoFen-3 (GF-3) satellite is the first polarimetric synthetic aperture radar (PolSAR) satellite in China. With a designed in-orbit life of 8 years, it will provide large amounts of PolSAR data for ocean monitoring, disaster reduction, and many other ...
Sha Jiang +4 more
doaj +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
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
Modifying the Yamaguchi Four-Component Decomposition Scattering Powers Using a Stochastic Distance
, 2015 Model-based decompositions have gained considerable attention after the initial work of Freeman and Durden. This decomposition which assumes the target to be reflection symmetric was later relaxed in the Yamaguchi et al.
Bhattacharya, Avik +4 more
core +1 more source
Oil Detection in a Coastal Marsh with Polarimetric Synthetic Aperture Radar (SAR) [PDF]
, 2011 The National Aeronautics and Space Administration’s airborne Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) was deployed in June 2010 in response to the Deepwater Horizon oil spill in the Gulf of Mexico.
Amina Rangoonwala +10 more
core +2 more sources
L‐Band InSAR Snow Water Equivalent Retrieval Uncertainty Increases With Forest Cover Fraction
Geophysical Research Letters, Volume 51, Issue 24, 28 December 2024.Abstract There is a pressing need for global monitoring of snow water equivalent (SWE) at high spatiotemporal resolution, and L‐band (1–2 GHz) interferometric synthetic aperture radar (InSAR) holds promise. However, the technique has not seen extensive evaluation in forests.
R. Bonnell +7 more
wiley +1 more source
European Journal of Remote Sensing, 2021 Synthetic aperture radar (SAR), with all-day and all-weather observation capabilities, can capture the phenology of crops with short growth cycles to improve land cover classification results.
Di Liu +5 more
doaj +1 more source