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Intelligent Image Semantic Segmentation: A Review Through Deep Learning Techniques for Remote Sensing Image Analysis

  • Published:2022-01-20 Issue: Volume: Page:
  • ISSN:0255-660X
  • Container-title:Journal of the Indian Society of Remote Sensing
  • language:en
  • Short-container-title:J Indian Soc Remote Sens

Author:

Jiang Baode,An Xiaoya,Xu Shaofen,Chen Zhanlong

Funder

China Scholarship Council, State Key Laboratory of Geo-Information Engineering

Key Laboratory of Geological Survey and Evaluation of Ministry of Education

the Fundamental Research Funds for the Central Universities, National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Earth and Planetary Sciences (miscellaneous),Geography, Planning and Development

Reference95 articles.

1. Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). SegNet: A deep convolutional encoder–decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615

2. Bona, D. S., Murni, A., & Mursanto, P. (2019). Semantic segmentation and segmentation refinement using machine learning case study: Water turbidity segmentation. In Proceedings of the 2019 IEEE international conference on aerospace electronics and remote sensing technology, ICARES 2019. https://doi.org/10.1109/ICARES.2019.8914551.

3. Chen, G., Zhang, X., Wang, Q., Dai, F., Gong, Y., & Zhu, K. (2018a). Symmetrical dense-shortcut deep fully convolutional networks for semantic segmentation of very-high-resolution remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5), 1633–1644. https://doi.org/10.1109/JSTARS.2018.2810320

4. Chen, J., Chen, G., Wang, L., Fang, B., Zhou, P., & Zhu, M. (2020). Coastal land cover classification of high-resolution remote sensing images using attention-driven context encoding network. Sensors (switzerland), 20(24), 1–22. https://doi.org/10.3390/s20247032

5. Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2014). Semantic image segmentation with deep convolutional nets and fully connected CRFs. Complex Variables, Theory and Application: An International Journal, 7(4), 357–361. https://doi.org/10.1080/17476938708814211

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