Skip to main content
SpringerLink
Log in

Remote Sensing Images Background Noise Processing Method for Ship Objects in Instance Segmentation

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Detection and segmentation of ship targets in remote sensing images is a research hotspot in the field of computer vision. However, due to the large coverage area of sea surface remote sensing images, the complex and changeable environment of the ship target, such as cloud interference, coastal buildings, and navigation ripples, the ship causes low detection and segmentation effect. In this paper, we propose an attention module-based method for background noise processing in remote sensing images. To solve the problem of complex background features and noise interference in remote sensing images, this paper introduces an attention module to suppress noise and other interfering features in the complex background by using the channel attention mechanism and spatial attention mechanism, which can enhance the network’s ability to extract object features, and improve the detection and segmentation effect of the network on remote sensing images. Firstly, we introduce Group Convolution into the original Residual Network to enhance the feature representation capability of the model. Secondly, the Swish activation function with better performance in the deep networks is introduced to replace the ReLU activation function in the original Residual Network to improve the accuracy of ship detection and segmentation. Finally, in view of the complex environment of ships in remote sensing images and the problem of noise interference, we introduce an attention mechanism to suppress the interference area and highlight the characteristics of ship areas. The experimental results show that with the improved method, the average accuracy (AP) of ship detection and segmentation has increased from 70.7% and 62.0% to 76.8% and 66.4%, respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig.1
Fig.2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Airbus. Airbus ship detection challenge[M/OL]. Accessed February 14, 2020. https //www. kaggle. com/c/airbus­ship­detection.

  • Chen L., Zhang H., Xiao J., Nie L., Shao J., Liu W., Chua T. (2017) SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning, In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21–26, pp. 6298–6306.

  • Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848.

    Article  Google Scholar 

  • Chu X., Yang W., Ouyang W., Ma C.,. Yuille A. L, Wang X. (2017) Multicontext attention for human pose estimation, In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, , pp. 5669–5678.

  • Feng, Y., Diao, W., Zhang, Y., Li, H., (2019) Ship Instance segmentation from remote sensing images using sequence local context module. IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, July 28-Aug 2, Yokohama Japan, 1025–1028.

  • He K., Gkioxari G., Dollar P., et al (2017) Mask R-CNN[C]. 2017 IEEE International Conference on Computer Vision (ICCV), Oct 22–29, Venice Italy: 2980–2988.

  • Hu J., Shen L., Sun G (2018) Squeeze-and-excitation networks, In Proceedings of the IEEE conference on computer vision and pattern recognition, 7132–7141.

  • Huang Z., Huang L., Gong Y., et al (2019) Mask Scoring R-CNN. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 16–20, LongBeach USA, 6409–6418.

  • Huang Z., Sun S., Li R (2020) Fast Single-Shot Ship Instance Segmentation Based on Polar Template Mask in Remote Sensing Images. IGARSS 2020–2020 IEEE International Geoscience and Remote Sensing Symposium, Sept 26-Oct 2, Waikoloa USA:1236–1239.

  • Li, K., Cheng, G., Bu, S., et al. (2017). Rotation-insensitive and context-augmented object detection in remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 56(4), 2337–2348.

  • Li, K., Cheng, G., Bu, S., & You, X. (2018). Rotation-insensitive and context285 augmented object detection in remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 56(4), 2337–2348.

    Article  Google Scholar 

  • Lin T. Y, Dollar P., Girshick R., et al (2017) Feature Pyramid Networks for Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition, July 22–25, Hawaii USA,: 936–944.

  • Liu S., Qi L., Qin H., et al (2018) Path Aggregation Network for Instance Segmentation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18–22, SaltLake USA, 8759–8768.

  • Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation, in. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, 3431–3440.

  • Nie, X., Duan, M., Ding, H., et al. (2020). Attention mask R-CNN for ship detection and segmentation from remote sensing images[J]. IEEE Access, 8, 9325–9334.

  • Noh, H., Hong, S., & Han, B. (2015). Learning deconvolution network for semantic 310 segmentation, in. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, 1520–1528.

  • Ramachandran P., Zoph B., Le Q V.Searching for activation functions[J].arXiv preprint arXiv:1710.05941, 2017.

  • Ren, S., He, K., Girshick, R., et al. (2017a). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149.

    Article  Google Scholar 

  • Ren, S., He, K., Girshick, R., Sun, J. (2017b). Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149.

  • Ronneberger O., Fischer P., Brox T (2015) U-net: Convolutional networks for biomedical image segmentation, In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241.

  • Sun, X., Wang, P., Yan, Z., et al. (2022). FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 184, 116–130.

  • Tang, T., Zhou, S., Deng, Z., et al. (2017). Vehicle detection in aerial images based on region convolutional neural networks and hard negative example mining[J]. Sensors, 17(2), 336.

  • Wang F., Jiang M., Qian C., Yang S., Li C., Zhang H., Wang X., Tang X (2017) Residual attention network for image classification, In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 6450–6458.

  • Xie E., Sun P., Song X., et al (2020) Polarmask: Single shot instance segmentation with polar representation. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, June 14–19, Seattle USA, 12193–12202.

  • Yang X., Yang J., Yan J., Zhang Y., Zhang T., Guo Z., Sun X., Fu K (2019) Scrdet: Towards more robust detection for small, cluttered and rotated objects, In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 8232–8241.

  • Zhao, H., Shi, J., Qi, X., et al. (2017). Pyramid scene parsing network[C]. Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2881–2890.

  • Zhao Liang et al. (2019) Research on Satellite Image Ship Detection Based on Mask R-CNN. Technology Vision (30):24-25. (in Chinese)

Download references

Author information

Authors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, Shaanxi Province, China

    Bosong Chai, Xuan Nie, Heyu Gao, Jianchao Jia & Qian Qiao

Authors
  1. Bosong Chai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuan Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Heyu Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianchao Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Qian Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuan Nie.

Ethics declarations

Conflict of interest

We have no conflict of interests to disclose. All co-authors are aware of the author list. All the authors agreed with the addition of authors in this paper, and all the authors agreed with the rearrangement of the names

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chai, B., Nie, X., Gao, H. et al. Remote Sensing Images Background Noise Processing Method for Ship Objects in Instance Segmentation. J Indian Soc Remote Sens 51, 647–659 (2023). https://doi.org/10.1007/s12524-022-01631-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-022-01631-7

Keywords

Search

Navigation