3-D wavelet image processing for spatial and spectral resolution of Landsat images

The purpose of this investigation is to apply 3-D wavelet denoising to resolve spatial, as well as spectral, data in Landsat images. The use of multiple thresholds will be extended to achieve image classification. Wavelet denoising has been shown to be effective for noise reduction in 1-D signals and 2-D images. 3-D wavelet transforms have the potential for multiresolution surface reconstruction from volume data. 3-D wavelet denoising will be applied to spatial (2-D) and spectral (1-D) data. Landsat images were produced from a multispectral scanner on Landsat satellites. Wavelet have been used to achieve some level of image classification. Finer classification can be achieved in agricultural areas because of temporal difference between crops and because of spectral differences in transmission spectra. Varying threshold should achieve image classification based on spectral difference between crops. 3-D wavelet data processing is expected to offer greater potential for improving resolution of volume data. Use of multithreshold for spectral resolution might be usefully applied to images generated by nonvisible wavelengths: radar, IR and laser radar.