Chinese Journal of Information Fusion
ISSN: 2998-3371 (Online) | ISSN: 2998-3363 (Print)
Email: [email protected]
[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.
Chinese Journal of Information Fusion
ISSN: 2998-3371 (Online) | ISSN: 2998-3363 (Print)
Email: [email protected]
Portico
All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/