Speckle reduction of SAR images based on signal subspace technique

In this paper, speckle removal from synthetic aperture radar (SAR) images using subspace-based technique is proposed. The fundamental principle is to decompose the vector space of the noisy image into signal-plus-noise subspace and the noise subspace. Noise reduction is achieved by removing the noise subspace and estimating the clean image from the remaining image subspace. Linear estimation of the clean image is performed by minimizing image distortion while maintaining the residual noise energy below some given threshold. Since the noise is considered to be additive with subspace technique, a homomorphic framework is used to convert the multiplicative speckle noise into additive. The performance of the proposed approach is tested with simulated images and with real SAR images, and compared with Lee filter. The results indicated significant improvements by the proposed technique in terms of structural similarity index measure (SSIM) and equivalent number of looks (ENL).