SAR polar format implementation with MATLAB

Transform coding based on the Karhunen-Loeve transform (KLT), the discrete cosine transform (DCT), and the discrete wavelet transform (DWT) is well-understood for optical images. Transform coding applied to synthetic aperture radar (SAR) data, however, has not been well-studied. This paper compares the results of compressing SAR images when it is available in Cartesian and polar formats. We compare the compression results based on six performance criteria-mean-squared error, mean absolute error, peak signal-to-noise ratio, energy compaction, transform gain, and compression ratio.In both the formats the phase information of the compressed data is preserved to a great extent. A block adaptive Max quantizer is used with 1-5 bit quantization of the components. The quality of the reconstructed data is compared in terms of compression ratio and quality parameters: signal to noise ratio (SNR), standard deviation of the phase (PSD), and mean phase error (MPE). The parameters are calculated for SAR raw data, complex data and 8-bit gray scale image. Finally, original(Fig.4) and reconstructed gray scale images(Fig. 5) are presented.