الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
 |  | from | 135 |  |  |
| | | from | 135 | | |
Abstract
The thesis is concerned with the problem of multispectral images compression. There are several well-known image compression techniques in the literature. The most common ones are: the Join Photographic Experts Group(JPEG), the three-dimensional (3D) Set Partitioning In Hierarchical Trees
(SPIHT), and (JPEG 2000). These techniques give a good compression performance. However, they suffer from some weakness such as low quality; they the spectral redundancy, and need to divide the input coding into nonoverlapping blocks in some transform domains (e.g. Discrete Cosine Transform DCT).In this thesis, we considered one type of multispectral images; Landsat Enhanced Thematic Mapper Plus (ETM+). The thesis presents three efficient multispectral image compression techniques aiming at reducing the size of storage of multispectral images with high-quality reconstruction. In the first proposed technique, a modified compression technique aiming at reducing the size of storage of multispectral images and maintaining at the same time the high-quality reconstruction is presented. An optimal multispectral band ordering process is applied before compression, and then, the Dual-Tree Discrete Wavelet Transform (DDWT) is used in the spectral dimension the 2D Discrete Wavelet Transform (DWT) is used in the spatial dimensions. Finally, a simple Huffman coder is used for compression. We presented a multispectral band ordering algorithm, and experimental results to demonstrate the validity of the band ordering. (This Technique has been published in Signal, Image and Video Processing, Springer, Volume 7, Issue 4, July 2013). IV In the second proposed technique, we used the correlation coefficients of bands to determine the most three correlated ones. Then, we applied 2D DWT to remove parts from the three bands that are very closed. Finally, we compressed the rest of multispectral bands by the traditional compression technique. (This Technique has been accepted in IET Image Processing Journal, 2013). The third proposed technique for the compression of distorted multispectral satellite images by noise. We first applied a filtering layer to denoising multispectral images then, we applied the compression process. (This Technique has been published in Applied Remote Sensing (JARS), SPIE, Volume 7, 2013). The performances of these compression techniques have been compared considering the distortion measurements; radiometric distortion and spectral distortion. The metrics; Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are used to measure the radiometric distortion. The Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) are used to measure the spectral distortion. Experimental results demonstrate that the proposed techniques have better performance than JPEG, JPEG 2000, SPIHT, and JPEG 2000 with 3D Dual-tree Wavelet transformation.