Results 11 to 20 of about 2,397 (207)

Polarimetric Convolutional Network for PolSAR Image Classification [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2019
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Xu Liu, Licheng Jiao, Xu Tang, Qigong Sun, Dan Zhang   +4 more
openaire   +4 more sources

PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network

Remote Sensing, 2021
Polarimetric synthetic aperture radar (PolSAR) image classification is one of the basic methods of PolSAR image interpretation. Deep learning algorithms, especially convolutional neural networks (CNNs), have been widely used in PolSAR image ...
Jianda Cheng, Fan Zhang, Deliang Xiang, Qiang Yin, Yongsheng Zhou, Wei Wang   +5 more
doaj   +3 more sources

Multi-Pixel Simultaneous Classification of PolSAR Image Using Convolutional Neural Networks

Sensors, 2018
Convolutional neural networks (CNN) have achieved great success in the optical image processing field. Because of the excellent performance of CNN, more and more methods based on CNN are applied to polarimetric synthetic aperture radar (PolSAR) image ...
Lei Wang, Xin Xu, Hao Dong, Rong Gui, Fangling Pu   +4 more
doaj   +3 more sources

Consistency Regularization Semisupervised Learning for PolSAR Image Classification

International Journal of Intelligent Systems
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, Shan Jiang, Weijie Li
openaire   +2 more sources

Covariance Symmetries Classification in Multitemporal/Multipass PolSAR Images [PDF]

IEEE Transactions on Geoscience and Remote Sensing
A polarimetric synthetic aperture radar (PolSAR) system, which uses multiple images acquired with different polarizations in both transmission and reception, has the potential to improve the description and interpretation of the observed scene. This is typically achieved by exploiting the polarimetric covariance or coherence matrix associated with each
Dehbia Hanis, Luca Pallotta, Augusto Aubry, Aichouche Belhadj-Aissa, Antonio De Maio   +4 more
openalex   +3 more sources

Multichannel semi-supervised active learning for PolSAR image classification

International Journal of Applied Earth Observations and Geoinformation
Deep neural networks have recently been extensively utilized for Polarimetric synthetic aperture radar (PolSAR) image classification. However, this heavily relies on extensive labeled data which is both costly and labor-intensive. To lower the collection
Wenqiang Hua, Yurong Zhang, Hongying Liu, Wen Xie, Xiaomin Jin   +4 more
doaj   +2 more sources

PolSAR Image Classification Via a Multigranularity Hybrid CNN-ViT Model With External Tokens and Cross-Attention [PDF]

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
With the development of deep learning technology, the application of convolutional neural network (CNN) and vision transformer (ViT) for polarimetric synthetic aperture radar (PolSAR) image classification has been deepened.
Wenke Wang, Jianlong Wang, Dou Quan, Meijuan Yang, Junding Sun, Bibo Lu   +5 more
doaj   +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

Fisher Vectors for PolSAR Image Classification [PDF]

IEEE Geoscience and Remote Sensing Letters, 2017
In this letter, we study the application of the Fisher vector (FV) to the problem of pixelwise supervised classification of polarimetric synthetic aperture radar images. This is a challenging problem since information in those images is encoded as complex-valued covariance matrices. We observe that the real parts of these matrices preserve the positive
Javier Redolfi, Jorge Sanchez, Ana Georgina Flesia   +2 more
openaire   +3 more sources

Complex-Valued Multi-Scale Fully Convolutional Network with Stacked-Dilated Convolution for PolSAR Image Classification

Remote Sensing, 2022
Polarimetric synthetic aperture radar (PolSAR) image classification is a pixel-wise issue, which has become increasingly prevalent in recent years. As a variant of the Convolutional Neural Network (CNN), the Fully Convolutional Network (FCN), which is ...
Wen Xie, Licheng Jiao, Wenqiang Hua
doaj   +1 more source