Deep learning methods have recently made substantial advances in polarimetric synthetic aperture radar (PolSAR) image classification. However, supervised training relying on massive labeled samples is one of its major limitations, especially for PolSAR ...
Bin Xi +5 more
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Assessment of GF3 Full-Polarimetric SAR Data for Dryland Crop Classification with Different Polarimetric Decomposition Methods. [PDF]
Sensors (Basel), 2022 Wang M +6 more
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Superpixel-based graph convolutional neural network for polarimetric synthetic aperture radar image classification. [PDF]
Sci RepImani M.
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Recent Advances in Deep Learning for SAR Images: Overview of Methods, Challenges, and Future Directions. [PDF]
Sensors (Basel)Peter E, Ang LM, Seng KP, Srivastava S.
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MSMTRIU-Net: Deep Learning-Based Method for Identifying Rice Cultivation Areas Using Multi-Source and Multi-Temporal Remote Sensing Images. [PDF]
Sensors (Basel)Wang M, Ma X, Zheng T, Su Z.
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Small-Target Detection Algorithm Based on Improved YOLOv11n. [PDF]
Sensors (Basel)Zeng K, Yu W, Qin X, Long S.
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A Review on PolSAR Decompositions for Feature Extraction. [PDF]
J ImagingKarachristos K +2 more
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Coastal Wetland Classification with GF-3 Polarimetric SAR Imagery by Using Object-Oriented Random Forest Algorithm. [PDF]
Sensors (Basel), 2021 Zhang X, Xu J, Chen Y, Xu K, Wang D.
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Machine learning prediction of future land surface temperature from SAR optical fusion under urban expansion in Changsha, China. [PDF]
Sci RepHe P, Chen Z, Zhang L, Ma C, Luo C.
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A Dual-Polarimetric SAR Ship Detection Dataset and a Memory-Augmented Autoencoder-Based Detection Method. [PDF]
Sensors (Basel), 2021 Hu Y, Li Y, Pan Z.
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