Results 11 to 20 of about 13,122,784 (192)

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   +4 more sources

Structure Label Matrix Completion for PolSAR Image Classification [PDF]

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   +3 more sources

PolSAR Image Classification Based on Statistical Distribution and MRF [PDF]

Remote Sensing, 2020
Classification is an important topic in synthetic aperture radar (SAR) image processing and interpretation. Because of speckle and imaging geometrical distortions, land cover mapping is always a challenging task especially in complex landscapes.
Junjun Yin 0001, Xiyun Liu, Jian Yang 0011, Chih-Yuan Chu 0002, Yang-Lang Chang   +4 more
openaire   +4 more sources

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

PolSAR Image Classification with Lightweight 3D Convolutional Networks [PDF]

Remote Sensing, 2020
Convolutional neural networks (CNNs) have become the state-of-the-art in optical image processing. Recently, CNNs have been used in polarimetric synthetic aperture radar (PolSAR) image classification and obtained promising results. Unlike optical images, the unique phase information of PolSAR data expresses the structure information of objects.
Hongwei Dong, Lamei Zhang, Bin Zou 0001
openaire   +4 more sources

A New Architecture of a Complex-Valued Convolutional Neural Network for PolSAR Image Classification

Remote Sensing, 2023
Polarimetric synthetic aperture radar (PolSAR) image classification has been an important area of research due to its wide range of applications. Traditional machine learning methods were insufficient in achieving satisfactory results before the advent ...
Yihui Ren, Wen Jiang, Ying Liu
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

Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification [PDF]

IEEE Transactions on Geoscience and Remote Sensing, 2022
The increasing applications of polarimetric synthetic aperture radar (PolSAR) image classification demand for effective superpixels’ algorithms. Fuzzy superpixels’ algorithms reduce the misclassification rate by dividing pixels into superpixels, which are groups of pixels of homogenous appearance and undetermined pixels.
Yuwei Guo 0001, Licheng Jiao, Rong Qu, Zhuangzhuang Sun, Shuang Wang 0001, Shuo Wang 0005, Fang Liu 0001   +6 more
openaire   +3 more sources

PolSAR image classification based on Laplacian Eigenmaps and superpixels [PDF]

EURASIP Journal on Wireless Communications and Networking, 2017
Abstract This paper proposes a method of polarimetric synthetic aperture radar (PolSAR) image classification using improved superpixel segmentation and manifold learning. Firstly, a 27-dimension polarimetric feature space is extracted by simple arithmetic operations of polarimetric parameters and polarimetric target decomposition.
Haijiang Wang 0001, Jinghong Han, Yangyang Deng   +2 more
openaire   +4 more sources

Learning Rotation Domain Deep Mutual Information Using Convolutional LSTM for Unsupervised PolSAR Image Classification

Remote Sensing, 2020
Deep learning can archive state-of-the-art performance in polarimetric synthetic aperture radar (PolSAR) image classification with plenty of labeled data.
Lei Wang, Xin Xu, Rong Gui, Rui Yang, Fangling Pu   +4 more
doaj   +2 more sources