Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

TL;DR

This paper introduces five statistical methods for predicting ground scenes in wavelength-resolution SAR images, with the median method showing the highest accuracy for change detection applications.

Contribution

The study compares five statistical approaches for ground scene prediction in SAR images and identifies the median method as the most effective for change detection.

Findings

Median method achieved 97% detection probability

False alarm rate was 0.11 per km^2

Predicted images effectively represent true ground scenes

Abstract

This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicate that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of $97\%$ and a false alarm rate of 0.11/km$^2, when considering military vehicles concealed in a forest.