Results 61 to 70 of about 2,015 (220)

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, Huanyu Liu, Junbao Li, Hasan Mir   +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, De, Shaunak, Frery, Alejandro C., Manickam, Surendar, Muhuri, Arnab   +4 more
core   +1 more source

Hierarchical Segmentation of Polarimetric SAR Images Using Heterogeneous Clutter Models [PDF]

, 2011
International audienceIn this paper, heterogeneous clutter models are used to describe polarimetric synthetic aperture radar (PolSAR) data. The KummerU distribution is introduced to model the PolSAR clutter.
Bombrun, Lionel, Gay, Michel, Totir, Felix, Vasile, Gabriel   +3 more
core   +4 more sources

Polarimetric SAR Image Classification Based on Ensemble Dual-Branch CNN and Superpixel Algorithm

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Recently, convolutional neural networks (CNNs) have been successfully utilized in polarimetric synthetic aperture radar (PolSAR) image classification and obtained promising results. However, most CNN-based classification methods require a large number of
Wenqiang Hua, Cong Zhang, Wen Xie, Xiaomin Jin   +3 more
doaj   +1 more source

Statistical modeling of polarimetric SAR data: a survey and challenges [PDF]

, 2017
Knowledge of the exact statistical properties of the signal plays an important role in the applications of Polarimetric Synthetic Aperture Radar (PolSAR) data. In the last three decades, a considerable research effort has been devoted to finding accurate
Chen, Jinsgon, Deng, Xinping, Han, Pengpeng, López Martínez, Carlos   +3 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, K. Elder, D. McGrath, H. P. Marshall, B. Starr, N. Adebisi, R. T. Palomaki, Z. Hoppinen   +7 more
wiley   +1 more source

CV-CPKAN: Complex-Valued Convolutional Kolmogorov–Arnold Framework for PolSAR Image Classification

Remote Sensing
Deep learning has significantly advanced PolSAR image processing, with a growing trend of integrating mathematical theories into deep neural networks to enhance their capabilities with regard to complex data.
Zuzheng Kuang, Shuxin Liu, Haixia Bi, Lijun He, Fan Li   +4 more
doaj   +1 more source

An optimised scattering power decomposition method oriented to ship detection in polarimetric synthetic aperture radar imagery

IET Radar, Sonar &Navigation, Volume 18, Issue 12, Page 2642-2656, December 2024.
An optimised scattering power decomposition model is proposed which comprises surface, double‐bounce, oriented dipole and volume scattering components. The authors derive the optimised four‐component decomposition model from mathematical and theoretical perspectives, and verify the rationality of the optimised decomposition model using large amounts of
Lu Fang, Wenxing Mu, Ning Wang, Tao Liu
wiley   +1 more source

Segmentation-Based PolSAR Image Classification Using Visual Features: RHLBP and Color Features

Remote Sensing, 2015
A segmentation-based fully-polarimetric synthetic aperture radar (PolSAR) image classification method that incorporates texture features and color features is designed and implemented.
Jian Cheng, Yaqi Ji, Haijun Liu
doaj   +1 more source

Entropy-based Statistical Analysis of PolSAR Data

, 2012
Images obtained from coherent illumination processes are contaminated with speckle noise, with polarimetric synthetic aperture radar (PolSAR) imagery as a prominent example.
Cintra, Renato J., Frery, Alejandro C., Nascimento, Abraão D. C.   +2 more
core   +1 more source