Results 11 to 20 of about 7,181 (241)

Improvement of PolSAR Decomposition Scattering Powers Using a Relative Decorrelation Measure [PDF]

, 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   +2 more sources

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   +2 more sources

Adversarial Reconstruction-Classification Networks for PolSAR Image Classification [PDF]

Remote Sensing, 2019
Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more widely used in recent years. It is well known that PolSAR image classification is a dense prediction problem. The recently proposed fully convolutional networks (
Yanqiao Chen, Yangyang Li, Licheng Jiao, Cheng Peng, Xiangrong Zhang, Ronghua Shang   +5 more
doaj   +2 more sources

GPU-Based Soil Parameter Parallel Inversion for PolSAR Data

Remote Sensing, 2020
With the development of polarimetric synthetic aperture radar (PolSAR), quantitative parameter inversion has been seen great progress, especially in the field of soil parameter inversion, which has achieved good results for applications.
Qiang Yin, You Wu, Fan Zhang, Yongsheng Zhou   +3 more
doaj   +1 more source

Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

ISPRS International Journal of Geo-Information, 2020
Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity.
Bo Cheng, Shiai Cui, Xiaoxiao Ma, Chenbin Liang   +3 more
doaj   +1 more source

Despeckling PolSAR Images with a Structure Tensor Filter [PDF]

IEEE Geoscience and Remote Sensing Letters, 2020
© 2004-2012 IEEE. In this letter, we propose a new despeckling filter for fully polarimetric synthetic aperture radar (PolSAR) images defined by 3× 3 complex Wishart distributions. We first generalize the well-known structure tensor to deal with PolSAR data which allows to efficiently measure the dominant direction and contrast of edges.
Daniel Santana-Cedres, Luis Gomez, Luis Alvarez, Alejandro C. Frery   +3 more
openaire   +3 more sources

Weakly Supervised Classification of PolSAR Images Based on Sample Refinement with Complex-Valued Convolutional Neural Network

Leida xuebao, 2020
In this study, a weakly supervised classification method is proposed to classify the Polarimetric Synthetic Aperture Radar (PolSAR) images based on sample refinement using a Complex-Valued Convolutional Neural Network (CV-CNN) to solve the problem that ...
QIN Xianxiang, YU Wangsheng, WANG Peng, CHEN Tianping, ZOU Huanxin   +4 more
doaj   +1 more source

A type of polarimetric parameter for evaluating the reliability of model-based decomposition result and its application

International Journal of Digital Earth, 2023
The reliability of the model-based decomposition result, which is seldom investigated, is a key factor in determining whether decomposition parameters can be effectively applied to polarimetric synthetic aperture radar (PolSAR) applications.
Wentao Han, Haiqiang Fu, Jianjun Zhu, Qinghua Xie   +3 more
doaj   +1 more source

Spatially adaptive polarimetric image despeckling using bandelet transform

European Journal of Remote Sensing, 2020
Polarimetric Synthetic Aperture Radar (PolSAR) imaging extended SAR applications by exploring the polarimetric properties of the target scatterers. Similar to SAR images, PolSAR images are prone to multiplicative speckle noise due to its coherent imaging
Roy Thankachan, R. Sethunadh., P. M. Ameer   +2 more
doaj   +1 more source

Speeding up Non-Gaussian POLSAR image analysis [PDF]

2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017
Non-Gaussian statistical models fit SAR data better than Gaussian-based statistics, in most cases, but are complicated and time-consuming to use for unsupervised image segmentation via probabilistic clustering. The more advanced the model, the more complicated and slow the clustering.
Doulgeris, Anthony Paul, Hu, Dingsheng
openaire   +2 more sources