Image Processing Techniques for Enhancing Satellite Imagery in Disaster Management
DOI:
https://doi.org/10.62951/ijcts.v1i1.60Keywords:
Image processing, satellite imagery, disaster management, deep learning, image segmentationAbstract
This study examines advanced image processing techniques to improve satellite imagery for use in disaster management and recovery efforts. Through methods like deep learning-based image segmentation and noise reduction, we enhance image clarity and detail, allowing for better decision-making in emergency response. The results indicate a significant improvement in identifying affected areas, aiding faster and more accurate response.
References
Barlow, J., et al. (2020). “The impact of the 2019 Amazon rainforest fires.” Environmental Research Letters, 15(12), 124002.
Buades, A., Coll, B., & Morel, J.-M. (2005). “A non-local algorithm for image denoising.” Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2, 60-65.
Chen, Y., et al. (2020). “Non-Local Means for satellite image denoising.” Remote Sensing, 12(10), 1615.
Liu, Y., et al. (2019). “Deep learning for flood mapping using satellite imagery.” ISPRS Journal of Photogrammetry and Remote Sensing, 150, 1-10.
McCarthy, M., et al. (2020). “Using GIS for disaster management during the Australian bushfires.” International Journal of Disaster Risk Reduction, 49, 101-112.
Miller, S., et al. (2016). “Assessing the impact of the 2010 Haiti earthquake using satellite imagery.” Geospatial Health, 11(2), 1-10.
Ronneberger, O., Fischer, P., & Becker, A. (2015). “U-Net: Convolutional networks for biomedical image segmentation.” Medical Image Computing and Computer-Assisted Intervention (MICCAI), 9351, 234-241.
UNDRR. (2020). “Global Assessment Report on Disaster Risk Reduction.” United Nations Office for Disaster Risk Reduction.
Yoshida, S., et al. (2017). “Damage assessment using deep learning for the 2011 Tōhoku earthquake.” Remote Sensing, 9(11), 1130.
Zhang, L., et al. (2021). “Deep learning-based denoising for satellite images.” IEEE Transactions on Geoscience and Remote Sensing, 59(5), 3890-3901.
Zhang, Y., et al. (2018). “Rapid assessment of flood damage using satellite imagery.” Remote Sensing of Environment, 210, 134-145.
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