Results 11 to 20 of about 11,244,032 (159)

A Novel Deep Fully Convolutional Network for PolSAR Image Classification

Remote Sensing, 2018
Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more popular in recent years. As we all know, PolSAR image classification is actually a dense prediction problem.
Yangyang Li, Yanqiao Chen, Guangyuan Liu, Licheng Jiao   +3 more
doaj   +2 more sources

Adversarial Reconstruction-Classification Networks for PolSAR Image Classification

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

Unsupervised PolSAR Image Classification Based on Superpixel Pseudo-Labels and a Similarity-Matching Network

Remote Sensing
Supervised polarimetric synthetic aperture radar (PolSAR) image classification demands a large amount of precisely labeled data. However, such data are difficult to obtain.
Lei Wang, Lingmu Peng, Rong Gui, Hanyu Hong, Shenghui Zhu   +4 more
doaj   +2 more sources

PolSAR Image Feature Extraction via Co-Regularized Graph Embedding

Remote Sensing, 2020
Dimensionality reduction (DR) methods based on graph embedding are widely used for feature extraction. For these methods, the weighted graph plays a vital role in the process of DR because it can characterize the data’s structure information.
Xiayuan Huang, Xiangli Nie, Hong Qiao
doaj   +2 more sources

Your Input Matters—Comparing Real-Valued PolSAR Data Representations for CNN-Based Segmentation

Remote Sensing, 2023
Inspired by the success of Convolutional Neural Network (CNN)-based deep learning methods for optical image segmentation, there is a growing interest in applying these methods to Polarimetric Synthetic Aperture Radar (PolSAR) data.
Sylvia Hochstuhl, Niklas Pfeffer, Antje Thiele, Horst Hammer, Stefan Hinz   +4 more
doaj   +2 more sources

Dual-Branch Fusion of Convolutional Neural Network and Graph Convolutional Network for PolSAR Image Classification

Remote Sensing, 2022
Polarimetric synthetic aperture radar (PolSAR) images contain useful information, which can lead to extensive land cover interpretation and a variety of output products.
Ali Radman, Masoud Mahdianpari, Brian Brisco, Bahram Salehi, Fariba Mohammadimanesh   +4 more
doaj   +2 more sources

Structure Label Matrix Completion for PolSAR Image Classification

Remote Sensing, 2020
Terrain classification is a hot topic in polarimetric synthetic aperture radar (PolSAR) image interpretation that aims at assigning a label to every pixel and forms a label matrix for a PolSAR image.
Qian Wu, Biao Hou, Zaidao Wen, Zhongle Ren, Bo Ren, Licheng Jiao   +5 more
doaj   +2 more sources

PolSAR Image Classification Based on Multi-Modal Contrastive Fully Convolutional Network

Remote Sensing
Deep neural networks have achieved remarkable results in the field of polarimetric synthetic aperture radar (PolSAR) image classification. However, PolSAR is affected by speckle imaging, resulting in PolSAR images usually containing a large amount of ...
Wenqiang Hua, Yi Wang, Sijia Yang, Xiaomin Jin   +3 more
doaj   +2 more sources

PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network

Remote Sensing
Polarimetric Synthetic Aperture Radar (PolSAR) image classification is one of the most important applications in remote sensing. The impressive superpixel generation approaches can improve the efficiency of the subsequent classification task and restrain
Mengxuan Zhang, Jingyuan Shi, Long Liu, Wenbo Zhang, Jie Feng, Jin Zhu, Boce Chu   +6 more
doaj   +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