A new method of remote sensing image recovery based on WWC

Xifang Zhu; Feng Wu; ChunKan Tao

doi:10.1117/12.806585

27 January 2009 A new method of remote sensing image recovery based on WWC

Xifang Zhu, Feng Wu, ChunKan Tao

Proceedings Volume 7156, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments; 71561P (2009) https://doi.org/10.1117/12.806585
Event: International Conference of Optical Instrument and Technology, 2008, Beijing, China

Abstract

By analyzing the multi-resolution characteristics of wavelet series, the frequency distribution of detail and approximation coefficients is deduced. It is concluded that the frequency of detail coefficients in low levels is higher than that in high levels, and the frequency of approximation coefficients is the lowest. According to this conclusion, this paper proposes a new method of remote sensing image recovery based on Weighted Wavelet Coefficient (WWC), namely, removing the cloud and mist from the remote sensing images using weighted wavelet coefficient algorithm. Suppose the image is decomposed by η levels with wavelet transform. By choosing reasonable level number l, the scenery information is mainly distributed to 1~l levels where detail coefficients have lower frequency and cloud and mist noise information are distributed mainly to l~n; levels where detail coefficients have relatively higher frequency. Approximation coefficients will also include cloud information. Detail coefficients in low levels and high levels and approximation coefficients are weighted with different factors. The scenery information is enhanced by increasing detail coefficients in low levels with a weight great than 1. The cloud and mist noise is weakened by decreasing detail coefficients in high levels with a weight less than 1. Approximation coefficients are weighted appropriately if including cloud. It is also proposed that the information entropy is taken as a criterion for choosing the number of the demarcation levels and the weighted factors. We have confirmed that our new algorithm is better than homomorphism filtering and Retinex algorithm by experiments.

Citation Download Citation

Xifang Zhu, Feng Wu, and ChunKan Tao "A new method of remote sensing image recovery based on WWC", Proc. SPIE 7156, 2008 International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments, 71561P (27 January 2009); https://doi.org/10.1117/12.806585

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KEYWORDS

Clouds

Fiber optic gyroscopes

Wavelets

Remote sensing

Image restoration

Linear filtering

Wavelet transforms

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