Polarimetric SAR image despeckling using bandelet transform based on additive–multiplicative speckle model

Abstract

Efficient speckle filtering algorithms are required for the effective use of polarimetric synthetic aperture radar (SAR) technology in remote sensing and surveillance applications. Nevertheless many techniques have been proposed over the past two decades to decrease the speckle noise in polarimetric SAR images, they are all based on the multiplicative speckle noise model. In order to fully utilise the advantages of polarimetry of these images, an additive–multiplicative noise model is explored. Coupled with this, bandelet based Bayesian thresholding is used to tap the advantages of transform domain filtering. Here the elements of the covariance matrix are processed differently for diagonal and off-diagonal elements to achieve maximum benefits. The proposed filtering scheme is evaluated using airborne and spaceborne polarimetric images and compared against state-of-the-art techniques. Results indicate that the proposed method reduces the speckle content while retaining the geometrical features in these images.