SEMI-SUPERVISED PolSAR IMAGE CLASSIFICATION BASED ON CROSSPSEUDO SUPERVISION AND HYBRID CONVOLUTIONAL NEURAL NETWORK

Fanfeng Shi; Fang Zheng

doi:10.2316/j.2026.201-0577

January 1, 2026Mechatronic systems and control

SEMI-SUPERVISED PolSAR IMAGE CLASSIFICATION BASED ON CROSSPSEUDO SUPERVISION AND HYBRID CONVOLUTIONAL NEURAL NETWORK

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Key Points

This research aims to enhance the effectiveness of image classification using semi-supervised learning techniques in PolSAR images.
Utilized a hybrid convolutional neural network (CNN) approach for image processing.
Implemented crosspseudo supervision for refining classification accuracy in the dataset.
Conducted comparisons with traditional labeled datasets to evaluate performance.
Significant improvement in classification accuracy compared to traditional methods.
Achieved effective performance even with reduced labeled data availability.

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Implication

Research demonstrates improved image classification in PolSAR using semi-supervised methods, suggesting greater accuracy.

Abstract

In computer vision applications, convolutional neural networks(CNNs) have demonstrated signiﬁcant eﬀectiveness and achievedremarkable success, largely because of the abundance of well-labelleddatasets.

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Cite This Study

Shi et al. (2026) studied this question.

synapsesocial.com/papers/699a9cc6482488d673cd28c5 https://doi.org/10.2316/j.2026.201-0577

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SEMI-SUPERVISED PolSAR IMAGE CLASSIFICATION BASED ON CROSSPSEUDO SUPERVISION AND HYBRID CONVOLUTIONAL NEURAL NETWORK

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

SEMI-SUPERVISED PolSAR IMAGE CLASSIFICATION BASED ON CROSSPSEUDO SUPERVISION AND HYBRID CONVOLUTIONAL NEURAL NETWORK

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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