De-noising remotely sensed digital imagery

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003 Pub Date : 2003-10-27 DOI:10.1109/WARSD.2003.1295193

S. Chettri, W. Campbell

引用次数: 4

Abstract

This paper applies two recent methods to denoise remotely sensed images - wavelet based Markov Random Field (MRF) methods, Independent Component Analysis (ICA) and compares them with the standard Wiener filter. In order to facilitate the continued use of these methods in remote sensing the theory behind each method is discussed in detail. Subsequently they are applied to de-noising remotely sensed images. The efficiency of each algorithm is obtained by computing the signal to noise ratio (SNR) before and after de-noising. Results indicate that the MRF based methods, though slightly more complicated to program and only marginally slower than ICA de-noising, generally perform better than both ICA and Wiener filtering. The article ends by discussing future areas of research in de-noising remotely sensed images.

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遥感数字图像去噪

本文应用了两种最新的遥感图像去噪方法--基于小波的马尔可夫随机场（MRF）方法和独立分量分析（ICA）方法，并将它们与标准的维纳滤波器进行了比较。为了便于在遥感领域继续使用这些方法，我们详细讨论了每种方法背后的理论。随后，将这些方法应用于遥感图像的去噪。通过计算去噪前后的信噪比（SNR），可以得出每种算法的效率。结果表明，基于 MRF 的方法虽然编程稍显复杂，速度也略低于 ICA 去噪方法，但总体上比 ICA 和维纳滤波方法都要好。文章最后讨论了遥感图像去噪的未来研究领域。

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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来源期刊

IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003

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