Open AccessReduction of Spatially Correlated Speckle in Textured SAR Images
Author(s) -
Oleksii Rubel,
Vladimir Lukin,
Sergiy Krivenko,
В. В. Павликов,
Simeon Zhyla,
Eduard Tserne
Publication year - 2021
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.20.3.2276
Subject(s) - computer science , speckle noise , artificial intelligence , speckle pattern , noise reduction , filter (signal processing) , discrete cosine transform , synthetic aperture radar , computer vision , noise (video) , pattern recognition (psychology) , median filter , reduction (mathematics) , gaussian noise , image (mathematics) , image processing , mathematics , geometry
Synthetic aperture radars (SARs) provide a lot of images that can be used for numerous applications. A problem with acquired images is that they are corrupted by speckle which is a noise-like phenomenon with multiplicative nature. In addition, speckle is non-Gaussian and it is often spatially correlated. A typical task in SAR image processing is despeckling and many methods have been already proposed. However, most of them do not take noise spatial correlation into account during denoising. In this paper, we show how this can be done in despeckling based on discrete cosine transform. The use of frequency-dependent thresholds leads to sufficient improvement of denoising efficiency in terms of visual quality metrics. Moreover, we consider quite complex structure texture images for which noise removal is usually problematic and can lead to information loss. Comparison to the well-known local statistic Lee and Frost filters, extended DCT-based filter is carried out for different remote sensing systems including Sentinel-1 and Sentinel-2.