-
CiteScore
1.83
Impact Factor
Chinese Journal of Information Fusion, 2024, Volume 1, Issue 2: 126-133

Free Access | Research Article | 29 September 2024
1 Naval Aviation University, Yantai 264001, China
* Corresponding author: Haoran Li, email: [email protected]
Received: 30 July 2024, Accepted: 25 September 2024, Published: 29 September 2024  

Abstract
Remote sensing image plays an important role in maritime surveillance, and as a result there is increasingly becoming a prominent focus on the detection and recognition of maritime objects. However, most existing studies in remote sensing image classification pay more attention on the performance of model, thus neglecting the transparency and explainability in it. To address the issue, an explainable classification method based on graph network is proposed in the present study, which seeks to make use of the relationship between objects' regions to infer the category information. First, the local visual attention module is designed to focus on different but important regions of the object. Then, graph network is used to explore the underlying relationships between them and further to get the discriminative feature. Finally, the loss function is constructed to provide a supervision signal to explicitly guide the attention maps and overall learning process of the model. Through these designs, the model could not only utilize the underlying relationships between regions but also provide explainable visual attention for people's understanding. Rigorous experiments on two public fine-grained ship classification datasets indicate that the classification performance and explainable ability of the designed method is highly competitive.

Graphical Abstract
Explainable Classification of Remote Sensing Ship Images Based on Graph Network

Keywords
Explainable visual feature
remote sensing image
ship classification

References

[1] Zhang, X., Lv, Y., Yao, L., Xiong, W., & Fu, C. (2020). A New Benchmark and an Attribute-Guided Multilevel Feature Representation Network for Fine-Grained Ship Classification in Optical Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1271-1285.

[2] Li, D., Liu, R., Tang, Y., & Liu, Y. (2024). PSCLI-TF: Position-Sensitive Cross-Layer Interactive Transformer Model for Remote Sensing Image Scene Classification. IEEE Geoscience and Remote Sensing Letters, 21, 1-5.

[3] Lan, J., & Wan, L. (2009). Automatic ship target classification based on aerial images. In 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments (Vol. 7156, p. 715612). SPIE.

[4] Xiong, W., Xiong, Z., Cui, Y., & Lv, Y. (2020). A Discriminative Distillation Network for Cross-Source Remote Sensing Image Retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1234-1247.

[5] Chen, J., Chen, K., Chen, H., Li, W., Zou, Z., & Shi, Z. (2022). Contrastive Learning for Fine-Grained Ship Classification in Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16.

[6] Chen, Y., Zhang, Z., Chen, Z., Zhang, Y., & Wang, J. (2022). Fine-Grained Classification of Optical Remote Sensing Ship Images Based on Deep Convolution Neural Network. Remote Sensing, 14(18), Article 18.

[7] Xiong, W., Xiong, Z., & Cui, Y. (2022). An Explainable Attention Network for Fine-Grained Ship Classification Using Remote-Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14.

[8] Zheng, H., Fu, J., Mei, T., & Luo, J. (2017). Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition. In 2017 IEEE International Conference on Computer Vision (ICCV), 5219-5227.

[9] Kipf, T. N., & Welling, M. (2016). Semi-Supervised Classification with Graph Convolutional Networks. arxiv preprint arxiv:1609.02907.

[10] Guo, Y., Bo, D., Yang, C., Lu, Z., Zhang, Z., Liu, J., Peng, Y., & Shi, C. (2023). Data-centric Graph Learning: A Survey. arxiv preprint arxiv:2310.04987.

[11] Yang, Y., Tang, X., Cheung, Y.-M., Zhang, X., & Jiao, L. (2023). SAGN: Semantic-Aware Graph Network for Remote Sensing Scene Classification. IEEE Transactions on Image Processing, 32, 1011-1025.

[12] Ge, Y., Xiao, Y., Xu, Z., Zheng, M., Karanam, S., Chen, T., Itti, L., & Wu, Z. (2021). A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2195-2204.

[13] Hu, H., Yao, M., He, F., & Zhang, F. (2022). Graph Neural Network via Edge Convolution for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 19, 1-5. IEEE Geoscience and Remote Sensing Letters.

[14] Di, Y., Jiang, Z., & Zhang, H. (2021). A Public Dataset for Fine-Grained Ship Classification in Optical Remote Sensing Images. Remote Sensing, 13(4), Article 4.

[15] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770-778.

[16] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2818-2826.

[17] Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2261-2269.

[18] Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arxiv preprint arxiv:1704.04861.

[19] Chollet, F. (2017). Xception: Deep Learning with Depthwise Separable Convolutions. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1800-1807.

[20] Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arxiv preprint arxiv:1409.1556.

[21] Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1492-1500).

[22] Lin, T.-Y., RoyChowdhury, A., & Maji, S. (2015). Bilinear CNN Models for Fine-Grained Visual Recognition. In 2015 IEEE International Conference on Computer Vision (ICCV), 1449-1457.

[23] Fu, J., Zheng, H., & Mei, T. (2017). Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4476-4484.

[24] Chen, Y., Bai, Y., Zhang, W., & Mei, T. (2019). Destruction and Construction Learning for Fine-Grained Image Recognition. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5152-5161.

[25] Zheng, H., Fu, J., Zha, Z.-J., & Luo, J. (2019). Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 5007-5016.

[26] Sun, H., He, X., & Peng, Y. (2022). SIM-Trans: Structure Information Modeling Transformer for Fine-grained Visual Categorization. In Proceedings of the 30th ACM International Conference on Multimedia, 5853-5861.

[27] Shi, Q., Li, W., & Tao, R. (2018). 2D-DFrFT Based Deep Network for Ship Classification in Remote Sensing Imagery. In 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), 1-5.

[28] Shi, Q., Li, W., Tao, R., Sun, X., & Gao, L. (2019). Ship Classification Based on Multifeature Ensemble with Convolutional Neural Network. Remote Sensing, 11(4), Article 4.


Cite This Article
APA Style
Li, H., Xiong, W., Cui, Y., & Xiong, Z. (2024). Explainable Classification of Remote Sensing Ship Images Based on Graph Network. Chinese Journal of Information Fusion, 1(2), 126–133. https://doi.org/10.62762/CJIF.2024.932552

Article Metrics
Citations:

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 520
PDF Downloads: 53

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Chinese Journal of Information Fusion

Chinese Journal of Information Fusion

ISSN: 2998-3371 (Online) | ISSN: 2998-3363 (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.