Study On SAR Data Compression Algorithms

Synthetic aperture radar (SAR) is an active microwave imaging sensor. It utilizes pulse compression technology to obtain high range resolution and aperture synthesis technology to achieve high azimuth resolution. SAR has a capability of obtaining high resolution that no ordinary radar can be comparable with, therefore, it is one of the significant directions for the modern radar development. At the same time, the microwave with long wavelength is able to penetrate foliage and the earth's surface, which is unsurpassable by other remote sensing ways, such as visible light and infrared ray. SAR has been applied widely to many fields of civil economy and national defense, such as terrain mapping, waterlog surveillance, ocean pollution surveillance, forest and crop investigation, military reconnaissance, etc.The SAR raw data is difficult to be transmitted and stored due to large data size, so it is necessary to use data compression or reduction techniques. In this thesis, the compression algorithms for SAR raw data are studied comprehensively and emphatically from the trade-off between performance and complexity. Some existing algorithms are improved and some new algorithms are put forward. Experimental results of these algorithms using live SAR raw data are given, and the performance and complexity of each algorithm is analyzed simultaneously.Chapter 1 firstly elaborates the role and signification of SAR raw data compression, and outlines the history of its development. Then, the statistical characteristics of SAR raw data are analyzed, and performance evaluation parameters in common use are introduced. At the same time, SAR raw data's entropy, probability distribution and the law of its variance are investigated respectively. Therefore, a conclusion is draw that the no-losing compression is not suitable for SAR raw data, and the losing compression has to be adopted. At the end of this chapter, the main contents and structure of this thesis are presented.In chapter 2, scalar quantization and vector quantization algorithms for SAR raw data compression are investigated. The structure of optimal scalar quantizer is analyzed. The algorithm of initial-codebook generation and three methods of codebook design are presented. On the basis of block adaptive quantization (BAQ) algorithm, an improved BAQ algorithm is put forward. Its performance is better on the condition of equal computational load. According to the amplitude-phase characteristics of SAR raw data, the amplitude trellis coded quantization-phase uniform quantization (ATCQ-PUQ) algorithm is investigated, in which the trellis coded quantization (TCQ) is applied to the amplitude, while the uniform quantization (UQ) is applied to the phase. It outperforms the improved BAQ algorithm. According to the three codebooks devised based on different principles, the block adaptive vector quantization (BAVQ) algorithm is analyzed. The encoding performance is improved much via vector quantization. At the end of this chapter, a combinational quantization algorithm, block adaptive scalar-vector quantization (BASVQ) algorithm is put forward. In this algorithm, the scalar quantization is adopted in the case that the data block satisfies Gaussian distribution. Otherwise the vector quantization is adopted. For the purpose of decreasing the computational load, bintree search is used in the course of vector quantization. The performance is improved with a little increase in computation.Transform coding algorithms for SAR raw data compression are studied in chapter 3. After analyzing the fast Fourier transform-block adaptive quantization (FFT-BAQ) algorithm, an improved FFT-BAQ algorithm is put forward. It makes bit allocation strategy in frequency domain more effective. Its performance is better on the condition of the same computational load. The discrete cosine transform-optimal entropy-constrained block adaptive quantization (DCT-OECBAQ) algorithm is investigated. The optimal entropy-constrained algorithm is able to design the scalar codebook with an arbitrary bit rate. Its performance is better than that of the improved BAQ algorithm. The wavelet transform-vector quantization (WT-VQ) algorithm is put forward. Furthermore, a coding strategy is proposed by analyzing the wavelet coefficients of the two-level wavelet transform. After two-level wavelet transform, no-losing coding is applied to the smooth subband and variable-rate vector quantization is applied to the one and two level detailed subband. Its performance is better than that of DCT-OECBAQ algorithm, but the computational load is rather high.In chapter 4, the compression algorithms for SAR raw data after range focusing are studied. The limitation of conventional SAR raw data compression is broken through by performing range focusing of SAR raw data firstly, which increases the azimuthal correlation and facilitates the compression for SAR data. In this chapter, the statistical characteristics of SAR raw data after range focusing are analyzed at first. According to that, a range focusing-linear prediction-block adaptive quantization (RF-LP-BAQ) algorithm is put forward. In the algorithm, the range focusing is performed for SAR raw data firstly, and the linear prediction is performed along the azimuth secondly, lastly, the block adaptive quantization is applied to the residual prediction series. In succession, a range focusing-Walsh-Hadamard transform-block adaptive quantization (RF-WHT-BAQ) algorithm is proposed. The algorithm fulfills the range focusing for SAR raw data firstly, and performs the two-dimension separable WHT secondly. At last, the block adaptive quantization is used in transform domain with Lloyd-Max quantizer. At the end of this chapter, range focusing-variable rate block adaptive vector quantization (RF-VRBAVQ) algorithm is put forward. It makes the best of the high correlation of the data in azimuth after range focusing. The long narrow block is adopted in azimuth, and the quantization vector is chosen along the azimuth. A reasonable bit allocation strategy is applied, and the variable-rate vector quantization is performed according to multiple codebooks. Although the performance of RF-LP-BAQ algorithm and RF-WHT-BAQ algorithm is not comparable with that of RF-VRBAVQ algorithm, their computational load is much less.Chapter 5 provides the conclusions of the algorithms proposed in this thesis, as well as the problems to be further studied.