TY - JOUR
T1 - Non-stationary speckle reduction in high resolution SAR images
AU - Xu, Zhihuo
AU - Shi, Quan
AU - Chen, Yunjin
AU - Feng, Wensen
AU - Shao, Yeqin
AU - Sun, Ling
AU - Huang, Xinming
PY - 2018/2/1
Y1 - 2018/2/1
N2 - This paper attempts to address non-stationary speckle reduction in high-resolution synthetic aperture radar (HR-SAR) images, using a novel Bayesian approach. First, non-stationary speckle is defined. Second, an innovative log-normal mixture model (LogNMM) is proposed to model the underlying data; the data priors are represented by using Fields of Experts (FoE); and then the despeckling model is derived based on maximum a posteriori (MAP) method. The experimental results demonstrate that the proposal produces state-of-the-art despeckling performance on synthetic and real HR-SAR data, and prove that the speckle is non-stationary in the HR-SAR data of interest.
AB - This paper attempts to address non-stationary speckle reduction in high-resolution synthetic aperture radar (HR-SAR) images, using a novel Bayesian approach. First, non-stationary speckle is defined. Second, an innovative log-normal mixture model (LogNMM) is proposed to model the underlying data; the data priors are represented by using Fields of Experts (FoE); and then the despeckling model is derived based on maximum a posteriori (MAP) method. The experimental results demonstrate that the proposal produces state-of-the-art despeckling performance on synthetic and real HR-SAR data, and prove that the speckle is non-stationary in the HR-SAR data of interest.
KW - Field of Experts (FoE)
KW - Log normal distribution mixture model (LogNMM)
KW - Maximum a posteriori (MAP)
KW - Speckle
KW - Synthetic aperture radar (SAR)
UR - http://www.scopus.com/inward/record.url?scp=85034752141&partnerID=8YFLogxK
U2 - 10.1016/j.dsp.2017.10.017
DO - 10.1016/j.dsp.2017.10.017
M3 - Article
AN - SCOPUS:85034752141
SN - 1051-2004
VL - 73
SP - 72
EP - 82
JO - Digital Signal Processing
JF - Digital Signal Processing
ER -