Abstract
Nowadays, many works are dedicated to improve the research results, previously achieved manually, by computational solutions. On light of this, the presented work aims to overcome a common problem in many satellite images, which is the presence of undesirable atmospheric components such as clouds and shadows at the time of scene capture. The presence of such elements hinders the identification of meaningful information for applications like urban and environmental monitoring, exploration of natural resources, etc. Thus, it is presented a new way to perform a hybrid approach toward removal and replacing of these elements. The authors propose a method of regions decomposition using a nonlinear median filter, in order to map regions of structure and texture. These types of regions will explain which method will be applied to region redefinition. At structure region, will be applied the method of inpainting by a smoothing based on DCT, and at texture one, will be applied the exemplar-based texture synthesis. To measure the effectiveness of this proposed technique, a qualitative assessment was presented, at the same time that a discussion about quantitative analysis was made.
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References
Bertalmio M, Sapiro G, Caselles V, Ballester C (2000) Image inpainting. In: Proceedings of the 27th annual conference on computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co, New York, pp 417–424
Bertalmio M, Vese L, Sapiro G, Osher S (2003) Simultaneous structure and texture image inpainting. IEEE Trans Image Process 12(8):882–889
Buckley M (1994) Fast computation of a discretized thin-plate smoothing spline for image data. Oxf Biometrika 81:247–258
Bugeau A, Bertalmio M (2009) Combining texture synthesis and diffusion for image inpainting. In: Ranchordas A, Araújo H (eds) VISAPP 2009—Proceedings of the fourth international conference on computer vision theory and applications, vol 1. INSTICC Press, Lisboa, pp 26–33
Criminisi A, Perez P, Toyama K (2004) Region filling and object removal by exemplar-based image inpainting. IEEE Trans On Image Process 13(9):1200–1212, IEEE Computer Society
Efros A, Leung T (1999) Texture synthesis by non-parametric sampling. In: International conference on computer vision. IEEE Computer Society, Washington, pp 1033–1038
Garcia D (2010) Robust smoothing of gridded data in one and higher dimensions with missing values. Comput Stat Data Anal 54(4):1167–1178 (Elsevier, Maryland Heights)
Hau CY, Liu CH, Chou TY, Yang LS (2008) The efficacy of semi-automatic classification result by using different cloud detection and diminution method. The international archives of the photogrammetry, remote sensing and spatial information sciences
Helmer E, Ruefenacht B (2005) Cloud-free satellite images mosaics with regression trees and histgram matching. Photogram Eng Remote Sens 32(9):1079–1089
Hoan NT, Tateishi R (2008) Cloud removal of optical image using SAR data for ALOS applications. Experimenting on simulated ALOS data. The international archives of the photogrammetry, remote sensing and spatial information sciences, Beijing
Liu H, Wang W, Bi X (2010) Study of image inpainting based on learning. In: proceedings of the international multi conference of engineers and computer scientists. Newswood Limited, Hong Kong, pp 1442–1445
Liu Y, Wong A, Fieguth P (2010) Remote sensing image synthesis. In: Geoscience and remote sensing symposium (IGARSS). IEEE International, Honolulu, pp 2467–2470
Maalouf A, Carre P, Augereau B, Fernandez Maloigne C (2009) A bandelet-based inpainting technique for clouds removal from remotely sensed images. IEEE Trans Geosci Remote Sens 47(7):2363–2371
Rudin LI, Osher S, Fatemi E (1992) North-Holland nonlinear total variation based noise removal algorithms. Phys D 60:259–268
Sarkar S, Healey G (2010) Hyperspectral texture synthesis using histogram and power spectral density matching. IEEE Trans Geosci Remote Sens 48(5):2261–2270
Siravenha A (2011) Um método para classificação de imagens de satélite usando Transformada Cosseno Discreta com detecção e remoção de nuvens e sombras. Universidade Federal do Pará, In Dissertação de mestrado
Taschler M (2006) A comparative analysis of image inpainting techniques. Technical report. The University of York, New York
Vese LA, Osher SJ (2002) Modeling textures with total variation minimization and oscillating patterns in image processing. J Sci Comput 19:553–572 (Plenum Press, New York)
Webster R, Oliver M (2007) Geostatistics for environmental scientists, 2nd edn. Wiley, West Sussex
Zhang X, Qin F, Qin Y (2010) Study on the thick cloud removal method based on multi-temporal remote sensing images. In: Conference international on multimedia technology (ICMT). IEEE, Ningbo, pp 1–3
Acknowledgments
This work was supported by the Amazon Research Foundation/Vale S/A [grant number 021/2010]; and the National Council of Technological and Scientific Development [grant number 142404/2011-0].
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Siravenha, A.C., Sousa, D., Pelaes, E. (2014). The Development of a Hybrid Solution to Replacement of Clouds and Shadows in Remote Sensing Images. In: Di Giamberardino, P., Iacoviello, D., Natal Jorge, R., Tavares, J. (eds) Computational Modeling of Objects Presented in Images. Lecture Notes in Computational Vision and Biomechanics, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-04039-4_15
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DOI: https://doi.org/10.1007/978-3-319-04039-4_15
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