الفهرس | Only 14 pages are availabe for public view |
Abstract In recent years, many satellites were launched, which provide various types of images about the earth surface. These images have different spectral and spatial characteristics. Image fusion techniques are used extensively to combine different images having complementary information into one single composite. The fused image has rich information that will improve the performance of image analysis algorithms. Pan-sharpening is a pixel level fusion technique used to increase the spatial resolution of the multispectral (MS) image using spatial information from the high resolution (HR) panchromatic (Pan) image while preserving the spectral information in the MS image. Super-Resolution (SR) is a fusion process for reconstructing an HR image from several low resolution (LR) images covering the same region in the world. If the images are sub-pixel shifted, then each LR image contains different information. SR extends the classical single-frame image reconstruction/restoration methods by simultaneously utilizing information from multiple observed images to achieve resolutions higher than those of the original images. The thesis firstly introduces the most popular existing Pan-sharpening algorithms. The performance of these algorithms varies both spectrally and spatially. The limitations of these algorithms are also analyzed. In addition, a new integrated adaptive principle component analysis (APCA) and High Pass Filtering (HPF) Pan-sharpening method is introduced and studied. Simulation results have shown that the proposed fusion method gives a compromise between the spectral and spatial quality, and at the same time has low computational and memory complexity. IV The optimal set of Non-Subsampled Contourlet Transform (NSCT) parameters (number of decomposition levels, number of orientations for different levels, different decomposition filters) for Pan-sharpening of remote sensing images with different spatial resolution ratios using hybrid APCA and NSCT methods are investigated. 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. Performance comparison of Pansharpening by the NSCT, the hybrid APCA/NSCT, the hybrid APCA/HPF, the APCA, the HPF, the Gram–Schmidt (GS), the (Discrete Wavelet Transform ) DWT, the Stationary Wavelet Transform (SWT), hybrid APCA/DWT, and hybrid APCA/SWT methods have been conducted. The results show that the Pansharpening method with APCA/HPF has the best spectral and spatial quality, and at the same time has low computational and memory complexity. Finally, new efficient techniques for sharpening Misrsat-1 data using SR and fusion methods are proposed. Due to the difference in spectral characteristics between bands 1, 3, and the Pan, we proposed two different methods to enhance the spatial resolution of Misrsat-1 data. The first method uses spatial-domain SR, in which SR is performed on the high-pass details extracted from bands 1, 3, and the Pan. The super-resolved high-pass details are used after that to enhance the spatial resolution of the MS data using the HPF fusion method. The second method depends on the interpolation of the high-frequency sub-band coefficients of the multi-scale representations of bands 1, 3, and the Pan band and the additive fusion method to add the high-frequency sub-band coefficients to different bands of the MS image. |