Results 21 to 30 of about 13,122,784 (192)
PolSAR Image Classification Based on Multi-Modal Contrastive Fully Convolutional Network
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 +3 more
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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 +3 more
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Object-Oriented Unsupervised Classification of PolSAR Images Based on Image Block
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
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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 +4 more
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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 +3 more
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PolSAR Image Feature Extraction via Co-Regularized Graph Embedding
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
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Segmentation-Based PolSAR Image Classification Using Visual Features: RHLBP and Color Features
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
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Multiobjective Evolutionary Superpixel Segmentation for PolSAR Image Classification
Superpixel segmentation has been widely used in the field of computer vision. The generations of PolSAR superpixels have also been widely studied for their feasibility and high efficiency. The initial numbers of PolSAR superpixels are usually designed manually by experience, which has a significant impact on the final performance of superpixel ...
Boce Chu +7 more
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Consistency Regularization Semisupervised Learning for PolSAR Image Classification
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 +3 more
wiley +2 more sources
An Unsupervised PolSAR Image Classification Algorithm Based on Tensor Product Graph Diffusion
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 +5 more
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