Using Neural Networks for Fast SAR Roughness Estimation of High Resolution Images

TL;DR

This paper introduces a neural network framework for rapid, reliable, and real-time estimation of SAR image roughness parameters, outperforming traditional methods especially for high-resolution data.

Contribution

The authors develop a neural network-based method that estimates SAR roughness parameters quickly and accurately, generalizing from synthetic training to real high-resolution images.

Findings

Neural network estimates are faster and more reliable than traditional methods.

The approach achieves real-time pixel-wise roughness estimation on high-resolution SAR images.

The method reduces estimation errors and failure rates.

Abstract

The analysis of Synthetic Aperture Radar (SAR) imagery is an important step in remote sensing applications, and it is a challenging problem due to its inherent speckle noise. One typical solution is to model the data using the $G_I^0$ distribution and extract its roughness information, which in turn can be used in posterior imaging tasks, such as segmentation, classification and interpretation. This leads to the need of quick and reliable estimation of the roughness parameter from SAR data, especially with high resolution images. Unfortunately, traditional parameter estimation procedures are slow and prone to estimation failures. In this work, we proposed a neural network-based estimation framework that first learns how to predict underlying parameters of $G_I^0$ samples and then can be used to estimate the roughness of unseen data. We show that this approach leads to an estimator that is quicker, yields less estimation error and is less prone to failures than the traditional estimation procedures for this problem, even when we use a simple network. More importantly, we show that this same methodology can be generalized to handle image inputs and, even if trained on purely synthetic data for a few seconds, is able to perform real time pixel-wise roughness estimation for high resolution real SAR imagery.