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Optical Remote Sensing Image Defogging Algorithm based on Different Wavelength

Published:07 September 2023Publication History

ABSTRACT

The shooting conditions of remote sensing images often cause them to be covered by clouds and fog, and remote sensing images handle more details more strictly, so it is very difficult to defog remote sensing images. In this paper, we propose an optical remote sensing image defogging algorithm based on different wavelengths. Firstly, the algorithm increases the transmittance attenuation value of the three channels to the initial energy value of the image based on different wavelength attenuation. Secondly, we use deep learning methods to segment the dense fog areas and determine the global atmosphere light candidate area to estimate the global atmosphere light. Finally, the proposed algorithm uses the atmospheric scattering model to defog the three channels respectively, and the final experimental results are obtained. Experimental results show that our method can achieve better defogging image effect and quality.

References

  1. Zhang Zheng, Li Qi, Xu Zhihai, Feng Huajun, and Chen Yueting. (2019). Remote sensing image dehazing by combining color lines and dark channels. Optics and Precision Engineering (1), 10.Google ScholarGoogle Scholar
  2. Arnfred, J. T., and Winkler, S. (2016). A general framework for image feature matching without geometric constraints. Pattern Recognition Letters, 73, 26-32.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. He, K., Sun, J., and Tang, X. (2010). Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence, 33(12), 2341-2353.Google ScholarGoogle Scholar
  4. Berman, D., and Avidan, S. (2016). Non-local image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1674-1682).Google ScholarGoogle ScholarCross RefCross Ref
  5. Chen, Z., Ou, B., and Tian, Q. (2019). An improved dark channel prior image defogging algorithm based on wavelength compensation. Earth Science Informatics, 12(4), 501-512.Google ScholarGoogle ScholarCross RefCross Ref
  6. Xie, F., Shi, M., Shi, Z., Yin, J., and Zhao, D. (2017). Multilevel cloud detection in remote sensing images based on deep learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(8), 3631-3640.Google ScholarGoogle ScholarCross RefCross Ref
  7. Tao, Y., Xu, M., Zhang, F., Du, B., and Zhang, L. (2017). Unsupervised-restricted deconvolutional neural network for very high resolution remote-sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(12), 6805-6823.Google ScholarGoogle ScholarCross RefCross Ref
  8. Luo, Y., Zhou, L., Wang, S., and Wang, Z. (2017). Video satellite imagery super resolution via convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 14(12), 2398-2402.Google ScholarGoogle ScholarCross RefCross Ref
  9. Li, E., Xia, J., Du, P., Lin, C., and Samat, A. (2017). Integrating multilayer features of convolutional neural networks for remote sensing scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55(10), 5653-5665.Google ScholarGoogle ScholarCross RefCross Ref
  10. Huang, B., Zhi, L., Yang, C., Sun, F., and Song, Y. (2020). Single satellite optical imagery dehazing using SAR image prior based on conditional generative adversarial networks. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 1806-1813).Google ScholarGoogle ScholarCross RefCross Ref
  11. Wieland, M., Li, Y., and Martinis, S. (2019). Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network. Remote Sensing of Environment, 230, 111203.Google ScholarGoogle ScholarCross RefCross Ref
  12. Yang, J., Guo, J., Yue, H., Liu, Z., Hu, H., and Li, K. (2019). CDnet: CNN-based cloud detection for remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 57(8), 6195-6211.Google ScholarGoogle ScholarCross RefCross Ref
  13. Zhang, H., and Patel, V. M. (2018). Densely connected pyramid dehazing network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3194-3203).Google ScholarGoogle ScholarCross RefCross Ref
  14. Xie, L., Wang, H., Wang, Z., and Cheng, L. (2020, July). DHD-Net: A Novel Deep-Learning-based Dehazing Network. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-7). IEEE.Google ScholarGoogle Scholar
  15. Qin, X., Wang, Z., Bai, Y., Xie, X., and Jia, H. (2020, April). FFA-Net: Feature fusion attention network for single image dehazing. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 34, No. 07, pp. 11908-11915).Google ScholarGoogle Scholar
  16. Ju Mingye, Zhang Dengyin, and Ji Yingtian. (2016). Image dehazing algorithm based on fog concentration estimation. Acta Automatica Sinica, 42(9), 13.Google ScholarGoogle Scholar
  17. Kang, L. W., Lin, C. W., and Fu, Y. H. (2011). Automatic single-image-based rain streaks removal via image decomposition. IEEE transactions on image processing, 21(4), 1742-1755.Google ScholarGoogle Scholar
  18. Grohnfeldt, C., Schmitt, M., and Zhu, X. (2018, July). A conditional generative adversarial network to fuse SAR and multispectral optical data for cloud removal from Sentinel-2 images. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 1726-1729). IEEE.Google ScholarGoogle Scholar
  19. Huang, B., Zhi, L., Yang, C., Sun, F., and Song, Y. (2020). Single satellite optical imagery dehazing using SAR image prior based on conditional generative adversarial networks. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 1806-1813).Google ScholarGoogle ScholarCross RefCross Ref

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      ICMLC '23: Proceedings of the 2023 15th International Conference on Machine Learning and Computing
      February 2023
      619 pages
      ISBN:9781450398411
      DOI:10.1145/3587716

      Copyright © 2023 ACM

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      Publication History

      • Published: 7 September 2023

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