Research On Wavelet-Based Sar Image Compression Algorithm

Synthetic aperture radar (SAR) is an active imaging microwave sensor which can be carried on a variety of airborne and spaceborne platforms. SAR is envolving to become an indispensable reconnaissance tool for military purposes because SAR can work under any times and any weathers. In the last few years, high spatial resolution images of the earth produced by SAR systems have large imaging size and the collection capacity for SAR images is increasing rapidly, thus SAR image transferring and restore become very challenging problems. Therefore, the research of SAR image compression with high fidelity is of great theoretical significance and application value, and is also an urgent task in the research and development of the SAR system.This thesis mainly studies the SAR image compression algorithms in wavelet domain focusing on the unique features of SAR image. Main work includes four aspects below:Firstly, the characteristics of SAR image are analyzed in detail, which affect the design of image compression algorithm. The first is speckle noise which reduces space correlation of image pixels, increases information entropy, and severely depresses SAR images quality. The second is that SAR images have both detailed texture information and many uniform regions. It is necessary to reduce bit rate of uniform regions. The last is that SAR image has high dynamic range This kind of remarkable difference means that those encoding/decoding algorithms for optical image data is not optimal for SAR data. It is necessary to design coding methods for SAR image combing with its unique characteristics.Secondly, SAR image compression algorithms based on wavelet transform are proposed. In following SAR image applications, speckle noise is first reduced. Based on the set partitioning in hierarchical trees (SPIHT) algorithm, speckle noise removal using spatial orientation trees (SOT) is introduced before coding to improve the quality of reconstructed SAR image; According to SAR image compression at high compression ratios, vector quantization (VQ) of wavelet trees decreases the block effects of VQ in space domain; At last, we study the effect of incorporating a human visual system (HVS)-based transform model in SPIHT algorithm to reduce visual redundancy and improve subjective perception quality for visual interpretation appliations.Thirdly, SAR image compression based on multiwavelet transform is proposed. Multiwavelet can possess desirable features simultaneously, such as the finite support, symmetry and orthogonality, while wavelet cannot. Thus multiwavelet has more advantages than wavelet in signal processing. Accroding to the feature of multiwavelet coefficients, we propose modified multiwavelet-based SPIHT algorithm to compress SAR image and obtain better reconstructed image quality than wavelet-based SPIHT algorithm. At the same time, we introduce modified soft-thresholding denoising method which suppresses the speckle noise while keeping edge well before image coding. Thus denosing and coding are joined in multiwavelet domain for SAR image to improve the reconstructed image quality. The peak signal to noise ratios of the reconstructed images are improved 1.5dB and 1.0dB.Last, SAR image compression methods based on wavelet packet are studied. SAR image has rich texture information which distuibute in middle and high frequency subbands. Wavelet transform just decomposes the low frequency subband, while wavelet packer transform also decomposes high frequency subbands and matches the energy distribution of signal better. We study the cost function of best base and propose the actual quantization bits as cost function combing the following coding method. Then different subbands are weighted according to importance and nonuniform quantization is realized to keep texture information of SAR image better.