Target feature enhancement of SAR image based on discrete shearlet transform

In addition to the inherent speckle noise, the Synthetic Aperture Radar (SAR) images exhibit low contrast and low signal-to-noise ratio (SNR), in some cases such as ocean monitoring with fierce wind. Given this problem, a novel method for target feature enhancement based on Discrete Shearlet Transform (DST) and multi-scale analysis theory is proposed in this paper. This approach captures the intrinsic geometrical features of target with discontinuities points in the SAR images effectively. In this work, the SAR image is decompose in multiple scales to get different sub-bands, the shearlet coefficients of images in different sub-bands with different directions are fusion. As the scale increases, the shearlet coefficient maximum of the target also increases, while the shearlet coefficient maximum of the speckle and clutter decreases. Therefore, the high-frequency features of different scales in different directions are fused, which makes not only the target enhanced but also the speckle and clutter suppressed. Experiments on ocean SAR images with strong speckle and clutter have been performed. Comparison with traditional wavelet approach, the results demonstrate that the proposed method is competitive in target feature enhancement and clutter suppression.