Image fusion of the multi-sensor lunar image data using wavelet combined transformation

Multisensor image fusion is the process of combining relevant information from high spatial resolution image and high spectral resolution image. This paper proposes a new image fusion method based on wavelet combined IHS and PCA transformations for remotely sensed lunar image data in order to extract features accurately. Different fusion techniques have been used in the past separately for spatial and spectral quality image enhancement. In this study, we use a new image fusion technique based on (1) Intensity Hue Saturation (IHS) combined wavelet transformation and (2) Principal Component Analysis (PCA) combined wavelet transformation. Results indicate that the fused lunar image shows good spatial fidelity and the spectral resolution of the fused product was preserved after image data fusion. It is seen that 97.4% or 97.82% of the spectral content is preserved by PCA combined wavelet fusion whereas IHS combined wavelet fusion shows 93.7% or 94.7% of spectral information. From the results of statistical evaluation parameters demonstrated for the two study sites, it is found that PCA combined wavelet transform gives better results than the other techniques commonly used.