2018/09/29 by Débora Chan, Andrea Rey, Chan, Débora +5
Computer Science · Engineering · Environmental Science · #Applications (stat.AP) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods #Soil Geostatistics and Mapping #Synthetic Aperture Radar (SAR) Applications and Techniques
paper · pdf · doi:10.48550/arxiv.1810.00216
openalex publication_date 2018/09/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The statistical properties of Synthetic Aperture Radar (SAR) image texture reveals useful target characteristics. It is well-known that these images are affected by speckle, and prone to contamination as double bounce and corner reflectors. The G0 distribution is flexible enough to model different degrees of texture in speckled data. It is indexed by three parameters: α, related to the texture, γ, a scale parameter, and L, the number of looks which is related to the signal-to-noise ratio. Quality estimation of α is essential due to its immediate interpretability. In this article, we compare the behavior of a number of parameter estimation techniques in the noisiest case, namely single look data. We evaluate them using Monte Carlo methods for non-contaminated and contaminated data, considering convergence rate, bias, mean squared error (MSE) and computational cost. The results are verified with simulated and actual SAR images.