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Published
Jun 29, 2014
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Abstract

In this paper, we  propose a cloud detection and removal approach based on information cloning. The approach removes cloud-contaminated portions of a satellite image and then reconstructs the information of missing data utilizing temporal correlation of multi temporal images[1]. The basic idea is to clone information from cloud-free patches to their corresponding cloud-contaminated patches under the assumption that land cover change insignificantly over a short period of time. Firstly, cloud is detected by using simple thresholding approach. Then a semi automatic approach is used to detect the cloud regions and the SSIM index for both the target and reference images is calculated to sort out according to image similarity. Finally the patch-based information reconstruction is mathematically formulated as a Poisson equation and solved using a global optimization process. Thus, the proposed approach can potentially yield better results in terms of radiometric accuracy and consistency.

Keywords: Cloud removal, information cloning, Poisson equation.

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Author Biographies

Bhavya V, Muslim Association College of Engineering, Trivandrum, Kerala, India

Department of Electronics and Communication Engineering

Shehna Jaleel, Muslim Association College of Engineering, Trivandrum, Kerala, India

Department of Electronics and Communication Engineering

Anu Sree. N.C, Muslim Association College of Engineering, Trivandrum, Kerala, India

Department of Electronics and Communication Engineering
How to Cite
V, B., Jaleel, S., & N.C, A. S. (2014). Cloud Removal From Multi-temporal Satellite Images Using Information Cloning and Information Reconstruction. International Journal of Emerging Trends in Science and Technology, 1(04). Retrieved from https://igmpublication.org/ijetst.in/index.php/ijetst/article/view/186

References

1. Chao-Hung Lin, Member, IEEE, Po-Hung Tsai, Kang-Hua Lai, and Jyun-Yuan Chen, “Coud removal from multitemporal satellite images using information cloning” ieee transactions on geoscience and remote sensing, vol. 51, no. 1, january 2013
2. J. Ju and D. P. Roy, “The availability of cloud-free Landsat ETM Plus data over the conterminous United States and globally,” Remote Sens. Environ., vol. 112, no. 3, pp. 1196–1211, 2008.
3. F. Melgani, “Contextual reconstruction of cloud-contaminated multitemporal multispectral images,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 2, pp. 442–455, Feb. 2006.
4. S. Benabdelkader and F. Melgani, “Contextual spatiospectral postreconstruction of cloud-contaminated images,” IEEE Geosci. Remote Sens. Lett., vol. 5, no. 2, pp. 204–208, Apr. 2008.
5. F. Chen, Z. Zhao, L. Peng, and D. Yan, “Clouds and cloud shadows removal from high-resolution remote sensing images,” in Proc. IEEE IGRASS, 2005, pp. 4256–4259.
6. Maalouf, P. Carre, B. Augereau, and C. Fernandez-Maloigne, “A bandelet-based inpainting technique for clouds removal from remotely sensed images,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 7, pp. 2363–2371, Jul. 2009.
7. Huang, N. Thomas, S. N. Goward, J. G. Masek, Z. Zhu, J. R. G.Townshend, and J. E. Vogelmann, “Automated masking of cloud and cloud shadow for forest change analysis using Landsat images,” Int. J. Remote Sens., vol. 31, no. 20, pp. 5449–5464, Jun. 2010.
8. P. Perez, M. Gangnet, and A. Blake, “Poisson image editing,” ACM Trans. Graph., vol. 22, no. 3, pp. 313–318, Jul. 2003.
9. J. Hays and A. A. Efros, “Scene completion using millions of photographs,”ACM Trans. Graph., vol. 26, no. 3, pp. 87–94, Jul. 2007.