Missing information in Remote Sensing: Wavelet approach to detect and remove clouds and their shadows

In this research the wavelet transform was applied to detect clouds and their shadows and subsequently fill out the missing information in a multitemporal set of Aster images of the north area of Ecuador. Wavelet theory is a powerful mathematical tool recently developed for signal processing. Remote sensing images can be considered as a signal. Furthermore, the wavelet transform is related to the concept of multi– resolution analysis where image are decomposed into successive scales or spatial resolutions. The first part of this research focuses on the detection of clouds and their shadows, applying three different methods involving wavelets and one non-wavelet approach. The first wavelet method was based on analysis of the energy or the wavelets for a pattern recognition approach. The second method used a stationary wavelet approach, the third method as well, but in a multi-scale product. Comparing the four methods, wavelet approaches did not perform better than the non-wavelet approach. In the second part of this research, wavelet image fusion was used to fill in the missing information and then the results were evaluated.