Abstract
This paper presents the evaluation of two approaches widely used in the inpainting literature, applied in the context of atmospheric noise removal, such as fog, dense and sparse clouds and shadows, which often occurs in remote sensing images. One approach uses the technique of nearest neighbor interpolation for the information dissemination by a DCT-based smoothing method, and the other is based on second-order partial differential equations methods that uses the heat diffusion and thin-plate spline methods, achieving their solutions by using the finite-difference method. Finally, the evaluation uses the Kappa coefficient and the PSNR index. The metrics indicate the effectiveness of the nearest neighbor interpolation strategy, which produces higher quality images, specially when comparing the results obtained by the use of differential equations modeled by thin-plate spline.
This work was supported by Fundação de Amparo à Pesquisa do Estado do Pará, grant no. 021/2008, Desenvolvimento de um sistema de interpretação de imagens de satélite baseado em modelos hÃbridos.
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Siravenha, A.C., Sousa, D., Bispo, A., Pelaes, E. (2011). Evaluating Inpainting Methods to the Satellite Images Clouds and Shadows Removing. In: Kim, Th., Adeli, H., Ramos, C., Kang, BH. (eds) Signal Processing, Image Processing and Pattern Recognition. SIP 2011. Communications in Computer and Information Science, vol 260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27183-0_7
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