Cloud Removal from Satellite Images Using Auto Associative Neural Network and Stationary Wevlet Transform

T. Sahoo, S. Patnaik
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引用次数: 13

Abstract

In this paper an image fusion technique is developed to remove clouds from satellite images. The proposed method involves an auto associative neural network based PCAT (principal component transform) and SWT (stationary wavelet transform) to remove clouds recursively which integrates complementary information to form a composite image from multitemporal images. Some evaluation measures are suggested and applied to compare our method with those of covariance based PCAT fusion method and WT-based one. The PSNR and the correlation coefficient value indicate that the performance of the proposed method is better than others. It also enhances the visual effect.
基于自关联神经网络和平稳小波变换的卫星图像去云
本文提出了一种图像融合技术来去除卫星图像中的云。该方法采用基于主成分变换(PCAT)和平稳小波变换(SWT)的自关联神经网络递归去除云,并将多时间点图像的互补信息整合成复合图像。提出了一些评价方法,并将其与基于协方差的PCAT融合方法和基于wt的PCAT融合方法进行了比较。PSNR和相关系数值表明,该方法的性能优于其他方法。它还增强了视觉效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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