A Review on Multispectral Satellite Image Dehazing Techniques



EOI: 10.11242/viva-tech.01.06.017

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Citation

Prasad Naik, Omkar Adarkar, Manas Mhatre, Prof. Krutika Vartak, "A Review on Multispectral Satellite Image Dehazing Techniques", VIVA-IJRI Volume 1, Issue 6, Article 17, pp. 1-9, 2023. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

The Multispectral Satellite Image Dehazing project endeavors to enhance the utility and quality of multispectral satellite imagery by developing specialized dehazing techniques tailored to the unique spectral characteristics of such data. Focusing on mitigating atmospheric interference, particularly haze, the project aims to improve data clarity, benefitting applications like environmental monitoring, disaster management, and land use analysis. The project's core objective is to address the challenges posed by haze in multispectral imagery, offering a significant contribution to remote sensing by advancing techniques specific to the nuances of multispectral satellite data. The outcomes hold the potential to elevate the accuracy and reliability of satellite-based information, impacting various fields such as agriculture, forestry, urban planning, and climate studies.

Keywords

Dehazing techniques, spectral characteristics, atmospheric interference, haze removal, data clarity, remote sensing, accuracy improvement.

References

  1. Yu, S., Seo, D. and Paik, J., 2023. Haze removal using deep convolutional neural network for Korea Multi-Purpose Satellite-3A (KOMPSAT-3A) multispectral remote sensing imagery. Engineering Applications of Artificial Intelligence, 123, p.106481.
  2. Ofir, N., 2023, June. Multispectral image fusion based on super pixel segmentation. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
  3. Y. Buhler, L. Meier and C. Ginzler, "Potential of Operational High Spatial Resolution Near-Infrared Remote Sensing Instruments for Snow Surface Type Mapping," in IEEE Geoscience and Remote Sensing Letters, vol. 12, no. 4, pp. 821-825, April 2015, doi: 10.1109/LGRS.2014.2363237.
  4. C. Neagoe, C. Vaduva and M. Datcu, "Haze and Smoke Removal for Visualization of Multispectral Images: A DNN Physics Aware Architecture," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 2102- 2105, doi: 10.1109/IGARSS47720.2021.9553735
  5. S. Khetkeeree, B. Petchthaweetham, S. Liangrocapart and S. Srisuk, "Sentinel-2 Image Dehazing using Correlation between Visible and Infrared Bands," 2020 8th International Electrical Engineering Congress (iEECON), Chiang Mai, Thailand, 2020, pp. 1-4, doi: 10.1109/iEECON48109.2020.229585.
  6. A. Kulkarni and S. Murala, "Aerial Image Dehazing with Attentive Deformable Transformers," 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2023, pp. 6294-6303, doi: 10.1109/WACV56688.2023.00624.
  7. S. Lolli, L. Alparone, A. Garzelli and G. Vivone, "Haze Correction for Contrast-Based Multispectral Pansharpening," in IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 12, pp. 2255-2259, Dec. 2017, doi: 10.1109/LGRS.2017.2761021.
  8. Makarau, A., Richter, R., Schläpfer, D. and Reinartz, P., 2016. Combined haze and cirrus removal for multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 13(3), pp.379-383.
  9. Mehta, A., Sinha, H., Mandal, M. and Narang, P., 2021. Domain-aware unsupervised hyperspectral reconstruction for aerial image dehazing. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 413-422).
  10. He, Z., Gong, C., Hu, Y. and Li, L., 2022. Remote sensing image dehazing based on an attention convolutional neural network. IEEE Access, 10, pp.68731-68739.
  11. Lee, S., Yun, S., Nam, J.H., Won, C.S. and Jung, S.W., 2016. A review on dark channel prior based image dehazing algorithms. EURASIP Journal on Image and Video Processing, 2016, pp.1-23.
  12. Mujbaile, D. and Rojatkar, D., 2020, March. Model based Dehazing Algorithms for Hazy Image Restoration–A Review. In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 142-148). IEEE.
  13. Wu, L., Chen, J., Chen, S., Yang, X., Xu, L., Zhang, Y. and Zhang, J., 2023. Hybrid Dark Channel Prior for Image Dehazing Based on Transmittance Estimation by Variant Genetic Algorithm. Applied Sciences, 13(8), p.4825.
  14. Sun, L.; Latifovic, R.; Pouliot, D.“Haze Removal Based on a Fully Automated and Improved Haze Optimized Transformation for Landsat Imagery over Land’. Remote Sens. 2017, 9, 972. https://doi.org/10.3390/rs9100972.
  15. Gu, Z.; Zhan, Z.; Yuan, Q.; Yan, L. “Single Remote Sensing Image Dehazing Using a Prior-Based Dense Attentive Network” Remote Sens. 2019, 11, 3008. https://doi.org/10.3390/rs11243008.