Abstract
Abstract. Synthetic aperture radar (SAR) image processing and analysis rely on statistical modeling and parameter estimation of the probability density functions that characterize data. The method of log-cumulants (MoLC) is a reliable alternative for parameter estimation of SAR data models and image processing. However, numerical methods are usually applied to estimate parameters using MoLC, and it may lead to a high computational cost. Thus, MoLC may be unsuitable for real-time SAR imagery applications such as change detection and marine search and rescue, for example. Our paper introduces a fast approach to overcome this limitation of MoLC, focusing on parameter estimation of single-channel SAR data modeled by the G0I distribution. Experiments with simulated and real SAR data demonstrate that our approach performs faster than MoLC, while the precision of the estimation is comparable with that of the original MoLC. We tested the fast approach with multitemporal data and applied the arithmetic-geometric distance to real SAR images for change detection on the ocean. The experiments showed that the fast MoLC outperformed the original estimation method with regard to the computational time.
Highlights
The processing and analysis of synthetic aperture radar (SAR) images are relevant for several remote sensing applications such as the monitoring of natural features and change detection on the Earth under certain weather conditions (Lopez-Martınez, Fabreagas, 2003),among other applications
Based on the definitions of the polygamma functions and gamma function (Arfken, Weber, 2005), we introduce an analytical formulation to lessen the computational time of the log-cumulants method to estimate the roughness and scale parameters of the G0I distribution
This section introduces our fast approach to the method of logcumulants (FAMoLC) to estimate the vector of parameters, (↵, )>, of the G0I distribution
Summary
The processing and analysis of synthetic aperture radar (SAR) images are relevant for several remote sensing applications such as the monitoring of natural features and change detection on the Earth under certain weather conditions (Lopez-Martınez, Fabreagas, 2003),among other applications. The method of log-cumulants (MoLC) is appropriate for parameter estimation of probability density functions that describe SAR data (Bujor et al, 2004), and it is well suited to a wide range of image processing algorithms and SAR applications (Bujor et al, 2004), (Krylov et al, 2013), (Rodrigues et al, 2016). Based on the definitions of the polygamma functions and gamma function (Arfken, Weber, 2005), we introduce an analytical formulation to lessen the computational time of the log-cumulants method to estimate the roughness and scale parameters of the G0I distribution. These estimates can be inputs to algorithms for intensity SAR images processing and classification. To evaluate the performance of the fast formulation, we performed experiments on synthetic and real SAR data to compare it with the original MoLC in terms of the computational time and estimation accuracy
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