Despeckling of polarimetric SAR images using optimized bandelet transform and GCV thresholding

Polarimetric synthetic aperture radar (PolSAR) imaging opened up a new era in remote sensing by exploiting the advantages of electromagnetic signals over optical signals. Much hype and hope exists over optical imaging due to its all weather imaging capability and capturing finer target details. However as in SAR imaging, PolSAR is also affected by speckle noise which is generally treated as multiplicative in nature. In order to extract target features, it is inevitable to filter out the speckle noise as part of preprocessing step in any image processing application. Transform domain filtering is desirable over spatial domain techniques due to the energy compaction property and easiness of filtering using thresholding. A novel method of despeckling using bandelet transform is proposed here as bandelets retain the geometrical features of the target effectively in comparison with other transforms post processing. Since the computational complexity involved with bandelet transform is relatively high compared to the state of the art techniques, a new approach using optimized bandelet transform based on directional variance coupled with optimal GCV threshold is proposed here. Performance comparison using both air borne and space borne real PolSAR images show that the proposed technique is much more effective and computationally efficient.