Multitemporal SAR image despeckling based on non-local theory

Abstract

In this paper, a multitemporal SAR image despeckling based on non-local theory (NLG-MulSAR) algorithm is proposed, which is improved based on the basic framework of the ratio-based multitemporal SAR image denoising (RABASAR). The temporal and spatial information of a multitemporal SAR image is integrated. The super image and the ratio image acquisition part of the RABASAR algorithm are optimized by the NLG filtering algorithm. The NLG algorithm does not need to transform multiplicative noise into additive noise on a synthetic aperture radar (SAR) image and then filter it. The NLG algorithm uses the nonlinear method to eliminate the influence of strong noise points while preserving image edge features. Based on the number of pixels in the non-local image block, the NLG algorithm avoids the generation of fuzzy noise on the filtered image. In this study, we use seven Gaofen three SAR images captured at different times in the Beijing area as experimental data to evaluate the effect of filtering methods in terms of five objective parameters: signal-to-noise ratio, standard deviation, equivalent number of looks, radiative resolution, and speckle noise index. In addition, based on the ratio image, we propose an index, namely the filtering edge coefficient of a multitemporal SAR image, to evaluate the filtering edge retention characteristics of a multitemporal SAR image. The results show that compared with the RABASAR filtering algorithm, the proposed NLG-MulSAR filtering algorithm can better balance the relationship between multiplicative noise and texture detail information and attenuate the speckle while protecting the texture detail information on the SAR image.