C14. Estimation of the optimal set of parameters for PAN-sharpening of satellite images based on the Non-Sub-sampled Contourlet Transform

Recent studies show that hybrid PAN-sharpening methods using the Non-Sub-sampled Contourlet Transform (NSCT) and classical PAN-sharpening methods like the Intensity, Hue and Saturation (IHS), Principal Component Analysis (PCA), and Adaptive Principal Component Analysis (APCA), reduce the spectral distortion in the PAN-sharpened images. The NSCT is a shift-invariant multi-resolution decomposition. It is based on Non-Sub-sampled Pyramid (NSP) decomposition and Non-Sub-sampled Directional Filter Banks (NSDFB). We compare the performance of the APCA-NSCT using different NSP filters, NSDFB filters, number of decomposition levels, and number of orientations in each level on Spot4 data with spatial resolution ratio 1/2, and QuickBird data with spatial resolution ratio 1/4. Experimental results show that the quality of PAN-sharpening of remote sensing images of different spatial resolution ratios using the APCA-NSCT method is affected by NSCT parameters. For the NSP, the `maxflat' filters have the best quality. For NSDFB the `sk' filters have the best quality. Changing the number of orientations in the same level of decomposition in the NSCT has a small effect on both the spectral and spatial quality. The spectral and spatial quality of PAN-sharpened images mainly depends on the number of decomposition levels. Too few decomposition levels result in poor spatial quality, while excessive levels of decomposition result in poor spectral quality.