A combined speckle noise reduction and, compression of SAR images using a multiwavelet based method to improve codec performance

SAR images are corrupted by multiplicative noise (speckle) which limits the performance of the classical coder/decoder (codec) in the spatial domain. Our objective is to give an evaluation of the efficiency of a multiwavelet transform coding algorithm. We use the additional degree of freedom offered by multiwavelets to fine tune the number of vanishing moments and the approximation order of their basis functions. Once the multiwavelet transform is performed, we apply an optimal bit allocation scheme on the subbands data using a set of vector quantizers. The quantization of the high frequencies multiwavelets coefficients may be though of as a hard thresholding algorithm. A measure of the equivalent number of looks is performed in the reconstructed SAR image in order to evaluate the impact of the codec in the noise reduction process. We compare our method with classical algorithm (baseline scalar wavelet transform followed by an optimal scalar quantization). The codec achieves comparable SNR, but performs surprising speckle noise reduction. Some results are presented with ERS-PRI images of Cameroon which can be compressed at 20 : 1 while still remaining of sufficient quality for visual interpretation, segmentation and land use monitoring.