Abstract
There are some problems arising from the classification of visible and infrared maritime ship, for example, the small number of image annotated samples and the low classification accuracy of feature concatenation fusion. To solve the problems, this paper proposes a dual-band ship decision-level fusion classification method based on multi-layer features and naive Bayesian model. To avoid the occurrence of over-fitting caused by the small number of annotated samples, the proposed method is adopted. First of all, a convolutional neural network (CNN) which has been pre-trained on ImageNet dataset is used and fine-tuned to extract convolutional features of dual-band images. Then, principal component analysis is conducted to reduce the dimension of convolutional feature while L2 normalization is applied to normalize the features after dimensionality reduction. Meanwhile, multi-layer convolutional feature fusion is conducted through the period. In doing so, not only storage and computing resources is reduced, the information of feature representation is also enriched. Finally, a Bayesian decision model is constructed using support vector machine and naive Bayesian theory, for the subsequent dual-band ship fusion classification. According to the experiments results on the public maritime ship dataset, the classification accuracy of the proposed decision-level fusion method reaches 89.8%, which is higher not only than that of the dual-band feature-level fusion by 1.0%–2.0%, but also than that of the state-of-the-art method by 1.6%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Oliveau, Q.: Ship classification for maritime surveillance. In: OCEANS 2019-Marseille, pp. 1–5. IEEE (2019)
Zhang, X., Lv, Y., Yao, L., Xiong, W., Fu, C.: A New benchmark and an attribute-guided multilevel feature representation network for fine-grained ship classification in optical remote sensing images. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 13, 1271–1285 (2020)
Zhenzhen, L., Baojun, Z., Linbo, T., Zhen, L., Fan, F.: Ship classification based on convolutional neural networks. J. Eng. 2019, 7343–7346 (2019)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Zhang, E., Wang, K., Lin, G.: Classification of marine vessels with multi-feature structure fusion. Appl. Sci. 9, 2153–2164 (2019)
Peng, C., Wang, N., Li, J., Gao, X.: DLFace: Deep local descriptor for cross-modality face recognition. Pattern Recogn. 90, 161–171 (2019)
Ding, L., Wang, Y., Laganiere, R., Huang, D., Fu, S.: Convolutional neural networks for multispectral pedestrian detection. Signal Process. Image Commun. 82, 115764–115779 (2020)
Zhang, Q., Huang, N., Yao, L., Zhang, D., Shan, C., Han, J.: RGB-T salient object detection via fusing multi-level CNN features. IEEE Trans. Image Process. 29, 3321–3335 (2019)
Zhang, H., Zhang, L., Zhuo, L., Zhang, J.: Object tracking in rgb-t videos using modal-aware attention network and competitive learning. Sensors 20, 393–348 (2020)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks (2014). arXiv preprint arXiv:1411.1792
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91–99 (2015)
Dao-Duc, C., Xiaohui, H., Morère, O.: Maritime vessel images classification using deep convolutional neural networks. In: Proceedings of the Sixth International Symposium on Information and Communication Technology, pp. 276–281 (2015)
Gundogdu, E., Solmaz, B., Yücesoy, V., Koç, A.: Marvel: a large-scale image dataset for maritime vessels. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10115, pp. 165–180. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-54193-8_11
Solmaz, B., Gundogdu, E., Yucesoy, V., Koc, A.: Generic and attribute-specific deep representations for maritime vessels. IPSJ Trans. Comput. Vision Appl. 9(1), 1–18 (2017). https://doi.org/10.1186/s41074-017-0033-4
Milicevic, M., Zubrinic, K., Obradovic, I., Sjekavica, T.: Application of transfer learning for fine-grained vessel classification using a limited dataset. In: Ntalianis, K., Vachtsevanos, G., Borne, P., Croitoru, A. (eds.) APSAC 2018. LNEE, vol. 574, pp. 125–131. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21507-1_19
Liu, Y., Cui, H.-Y., Kuang, Z., Li, G.-Q.: Ship detection and classification on optical remote sensing images using deep learning. In: ITM Web of Conferences, pp. 5012–5025. EDP Sciences (2017)
Zhang, M.M., Choi, J., Daniilidis, K., Wolf, M.T., Kanan, C.: VAIS: a dataset for recognizing maritime imagery in the visible and infrared spectrums. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 10–16 (2015)
Shi, Q., Li, W., Zhang, F., Hu, W., Sun, X., Gao, L.: Deep CNN with multi-scale rotation invariance features for ship classification. IEEE Access 6, 38656–38668 (2018)
Huang, L., Li, W., Chen, C., Zhang, F., Lang, H.: Multiple features learning for ship classification in optical imagery. Multimedia Tools Appl. 77(11), 13363–13389 (2017). https://doi.org/10.1007/s11042-017-4952-y
Shi, Q., Li, W., Tao, R., Sun, X., Gao, L.: Ship classification based on multifeature ensemble with convolutional neural network. Remote Sens. 11, 419 (2019)
Aziz, K., Bouchara, F.: Multimodal deep learning for robust recognizing maritime imagery in the visible and infrared spectrums. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 235–244. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93000-8_27
Santos, C.E., Bhanu, B.: Dyfusion: dynamic IR/RGB fusion for maritime vessel recognition. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 1328–1332. IEEE (2018)
Li, C., Ren, J., Huang, H., Wang, B., Zhu, Y., Hu, H.: PCA and deep learning based myoelectric grasping control of a prosthetic hand. Biomed. Eng. Online 17, 1–18 (2018)
Sun, Y., et al.: Image classification base on PCA of multi-view deep representation. J. Vis. Commun. Image Represent. 62, 253–258 (2019)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, Z. et al. (2021). Dual-Band Maritime Ship Classification Based on Multi-layer Convolutional Features and Bayesian Decision. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-92185-9_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-92184-2
Online ISBN: 978-3-030-92185-9
eBook Packages: Computer ScienceComputer Science (R0)