Next Article in Journal
Comparison of GOES16 Data with the TRACER-ESCAPE Field Campaign Dataset for Convection Characterization: A Selection of Case Studies and Lessons Learnt
Previous Article in Journal
Strategies for Soil Salinity Mapping Using Remote Sensing and Machine Learning in the Yellow River Delta
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

CCDR: Combining Channel-Wise Convolutional Local Perception, Detachable Self-Attention, and a Residual Feedforward Network for PolSAR Image Classification

1
School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, China
2
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China
3
School of Software, Henan Polytechnic University, Jiaozuo 454003, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(15), 2620; https://doi.org/10.3390/rs17152620
Submission received: 16 May 2025 / Revised: 16 July 2025 / Accepted: 25 July 2025 / Published: 28 July 2025
(This article belongs to the Section Remote Sensing Image Processing)

Abstract

In the task of PolSAR image classification, effectively utilizing convolutional neural networks and vision transformer models with limited labeled data poses a critical challenge. This article proposes a novel method for PolSAR image classification that combines channel-wise convolutional local perception, detachable self-attention, and a residual feedforward network. Specifically, the proposed method comprises several key modules. In the channel-wise convolutional local perception module, channel-wise convolution operations enable accurate extraction of local features from different channels of PolSAR images. The local residual connections further enhance these extracted features, providing more discriminative information for subsequent processing. Additionally, the detachable self-attention mechanism plays a pivotal role: it facilitates effective interaction between local and global information, enabling the model to comprehensively perceive features across different scales, thereby improving classification accuracy and robustness. Subsequently, replacing the conventional feedforward network with a residual feedforward network that incorporates residual structures aids the model in better representing local features, further enhances the capability of cross-layer gradient propagation, and effectively alleviates the problem of vanishing gradients during the training of deep networks. In the final classification stage, two fully connected layers with dropout prevent overfitting, while softmax generates predictions. The proposed method was validated on the AIRSAR Flevoland, RADARSAT-2 San Francisco, and RADARSAT-2 Xi’an datasets. The experimental results demonstrate that the proposed method can attain a high level of classification performance even with a limited amount of labeled data, and the model is relatively stable. Furthermore, the proposed method has lower computational costs than comparative methods.
Keywords: polarimetric synthetic aperture radar image classification; convolutional neural network; vision transformer; detachable self-attention mechanism; global–local feature interaction polarimetric synthetic aperture radar image classification; convolutional neural network; vision transformer; detachable self-attention mechanism; global–local feature interaction

Share and Cite

MDPI and ACS Style

Wang, J.; Zhang, B.; Xu, Z.; Sima, H.; Sun, J. CCDR: Combining Channel-Wise Convolutional Local Perception, Detachable Self-Attention, and a Residual Feedforward Network for PolSAR Image Classification. Remote Sens. 2025, 17, 2620. https://doi.org/10.3390/rs17152620

AMA Style

Wang J, Zhang B, Xu Z, Sima H, Sun J. CCDR: Combining Channel-Wise Convolutional Local Perception, Detachable Self-Attention, and a Residual Feedforward Network for PolSAR Image Classification. Remote Sensing. 2025; 17(15):2620. https://doi.org/10.3390/rs17152620

Chicago/Turabian Style

Wang, Jianlong, Bingjie Zhang, Zhaozhao Xu, Haifeng Sima, and Junding Sun. 2025. "CCDR: Combining Channel-Wise Convolutional Local Perception, Detachable Self-Attention, and a Residual Feedforward Network for PolSAR Image Classification" Remote Sensing 17, no. 15: 2620. https://doi.org/10.3390/rs17152620

APA Style

Wang, J., Zhang, B., Xu, Z., Sima, H., & Sun, J. (2025). CCDR: Combining Channel-Wise Convolutional Local Perception, Detachable Self-Attention, and a Residual Feedforward Network for PolSAR Image Classification. Remote Sensing, 17(15), 2620. https://doi.org/10.3390/rs17152620

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop