SAR Raw Data Compression Based On The Compressed Sensing Technology

With the development of Synthetic aperture radar (SAR) into multi-band, multi-polarization, and varieties of modes of ultra-high resolution imaging radar, the main problem increased is the huge amount of raw data for transmission and storage. Effective compression on the SAR raw data, is one of the effective solutions to this problem. This dissertation is focus on a new SAR raw data compression algorithm - based on compressed sensing theory of SAR raw data compression.Firstly, this dissertation analyzes the characteristics of SAR raw data, including statistical characteristics, spectrum, correlation, introduces the quantitative coding, reviews of two classic SAR raw data compression algorithms. In this dissertation, a block adaptive quantization coding algorithm and the wavelet transform with block adaptive quantization coding combination algorithm- SAR raw data compression based on wavelet transform are studied. Then carries on effective compression for SAR Raw data and gives a detailed appraisal by offering some evaluation results.Then, the dissertation introduces latest theoretical-compressed sensing framework, analyzes the SAR signal's sparse representation and conditions compressed sensing theory is used in the application of SAR raw data .In the study on the compression of SAR raw data compression based on compressed sensing, we consider the selection of the original data, data collection and recovery. SAR imaging algorithm will be seen as a transformation base here. Combining the SAR imaging algorithm and theory of compressed sensing, we achieve effective compression of SAR raw data compression based on compressed sensing with comparison with two classic algorithms.Experimental results show that SAR raw data compression based on the compressed sensing, can remain most important retain target information of the image with efficient data reduction.