Results 51 to 60 of about 5,464 (214)

POLSAR Image Classification via Clustering-WAE Classification Model

IEEE Access, 2018
Considering the clustering algorithms could explore the label information automatically, this paper proposes a new method in terms of polarimetric synthetic aperture radar (POLSAR) image classification, which named a clustering-wishart-auto-encoder (WAE)
Wen Xie, Ziwei Xie, Feng Zhao, Bo Ren
doaj   +1 more source

Unsupervised PolSAR Change Detection Based on Polarimetric Distance Measurements and ConvLSTM Network

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Time-series PolSAR are capable for continuous change monitoring of natural resources and urban land-covers regardless of weather and lighting conditions.
Rong Gui, Xinyue Zhang, Jun Hu, Lei Wang, Xing Zhang   +4 more
doaj   +1 more source

Improvement of PolSAR Decomposition Scattering Powers Using a Relative Decorrelation Measure

, 2017
In this letter, a methodology is proposed to improve the scattering powers obtained from model-based decomposition using Polarimetric Synthetic Aperture Radar (PolSAR) data.
Bhattacharya, A., Ratha, D., Surendar, M.   +2 more
core   +1 more source

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., Freeman, A., Li, Z., Pi, X., Rosen, P.   +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, Deliang Xiang, Zhiyuan Lin, Carlos Lopez-Martinez, Wei Hu, Fan Zhang   +5 more
openaire   +3 more sources

Interlinked Dynamics of Rice Cultivation Intensity and Ground Subsidence: Spatio‐Temporal Insights Into Water Resource Implications From Multi‐Year Spaceborne SAR Analysis

Water Resources Research, Volume 62, Issue 4, April 2026.
Abstract Sustainable rice cultivation is vital to meet global food demands, particularly in regions relying on groundwater for irrigation. Over‐pumping, however, can cause significant ground subsidence, threatening both infrastructure and agricultural sustainability.
Ya‐Lun S. Tsai, Xun‐Yan Liu
wiley   +1 more source

Polarimetric Synthetic Aperture Radar Speckle Filter Based on Joint Similarity Measurement Criterion

Remote Sensing, 2023
Polarimetric Synthetic Aperture Radar (PolSAR) data is inherently characterized by speckle noise, which significantly deteriorates certain aspects of the quality of the PolSAR data processing, including the polarimetric decomposition and target ...
Fanyi Tang, Zhenfang Li, Qingjun Zhang, Zhiyong Suo, Zexi Zhang, Chao Xing, Huancheng Guo   +6 more
doaj   +1 more source

Exploring Polarimetric Properties Preservation for PolSAR Image Reconstruction With Complex‐Valued Convolutional Neural Networks

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, Joana Frontera‐Pons, Jérémy Fix, Chengfang Ren, Jean‐Philippe Ovarlez   +4 more
wiley   +1 more source

Classification of Complex Wishart Matrices with a Diffusion-Reaction System guided by Stochastic Distances

, 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, Frery, Alejandro C., Gomez, Luis, Mazorra, Luis   +3 more
core   +1 more source

Using Texture‐Based Image Segmentation and Machine Learning With High‐Resolution Satellite Imagery to Assess Permafrost Degradation Landforms in the Russian High Arctic

Journal of Geophysical Research: Machine Learning and Computation, Volume 2, Issue 3, September 2025.
Abstract Amplified climate change across the Arctic causes significant permafrost thaw and an increase of permafrost degradation landforms. These landforms range from fine‐scale degrading ice wedge‐polygon‐networks to large‐scale features such as thermo‐erosional gullies and reshape entire landscapes.
Cornelia M. Inauen, Ingmar Nitze, Moritz Langer, Anne Morgenstern, Irena Hajnsek, Guido Grosse   +5 more
wiley   +1 more source