Results 21 to 30 of about 12,268,087 (190)

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

A Novel Classification Method for PolSAR Image Combining the Deep Learning Model and Adaptive Boosting of Shallow Classifiers

Canadian Journal of Remote Sensing, 2023
Polarimetric synthetic aperture radar (PolSAR) images are classified mainly according to the backscattering information of ground objects. For regions with complex backscattering information, misclassification is easy to occur, which leads to challenges ...
Yan Duan, Shaojie Bai, Limin Liu, Guangwei Wang   +3 more
doaj   +2 more sources

An Unsupervised PolSAR Image Classification Algorithm Based on Tensor Product Graph Diffusion

Leida xuebao, 2019
To overcome the difficulty of similarity expression and the effects of speckle noise in unsupervised classification of Polarimetric Synthetic Aperture Radar (PolSAR) images, a novel unsupervised PolSAR image terrain classification algorithm based on ...
ZOU Huanxin, LI Meilin, MA Qian, SUN Jiachi, CAO Xu, QIN Xianxiang   +5 more
doaj   +2 more sources

Object-Oriented Unsupervised Classification of PolSAR Images Based on Image Block

Remote Sensing, 2022
Land Use and Land Cover (LULC) classification is one of the tasks of Polarimetric Synthetic Aperture Radar (PolSAR) images’ interpretation, and the classification performance of existing algorithms is highly sensitive to the class number, which is inconsistent with the reality that LULC classification should have multiple levels of detail in the same ...
Binbin Han, Ping Han, Zheng Cheng
openaire   +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 0017, Shan Jiang 0023, Weijie Li
openaire   +2 more sources

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   +2 more sources

A Hierarchical Fully Convolutional Network Integrated with Sparse and Low-Rank Subspace Representations for PolSAR Imagery Classification

Remote Sensing, 2018
Inspired by enormous success of fully convolutional network (FCN) in semantic segmentation, as well as the similarity between semantic segmentation and pixel-by-pixel polarimetric synthetic aperture radar (PolSAR) image classification, exploring how to ...
Yan Wang, Chu He, Xinlong Liu, Mingsheng Liao   +3 more
doaj   +2 more sources

TCSPANet: Two-Staged Contrastive Learning and Sub-Patch Attention Based Network for PolSAR Image Classification

Remote Sensing, 2022
Polarimetric synthetic aperture radar (PolSAR) image classification has achieved great progress, but there still exist some obstacles. On the one hand, a large amount of PolSAR data is captured.
Yuanhao Cui, Fang Liu, Xu Liu, Lingling Li, Xiaoxue Qian   +4 more
doaj   +2 more sources

Gaofen-3 PolSAR Image Classification via XGBoost and Polarimetric Spatial Information. [PDF]

Sensors (Basel), 2018
Dong H, Xu X, Wang L, Pu F.
europepmc   +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