A wavelet schrinkage approach to detect candidate point scatterers in synthetic aperture sonar images for resolution estimation

Abstract

Autonomous Underwater Vehicles (AUVs) that are equipped with side-looking synthetic aperture sonar (SAS) have become a popular asset for efficient high resolution imaging and mapping of the seafloor. There are situations where it can be challenging to produce SAS images of good quality. For instance, this depends on the ocean environment, vehicle stability, navigation accuracy or seafloor bathymetry. For running operations more autonomously, an AUV must be able to estimate its sonar performance through-the-sensor such that it can automatically adjust track orientation, processing techniques or other parameters to improve performance. Hence, it is imperative to develop techniques to assess image quality from the imagery itself. Given sufficient signal to noise ratio, we recognize image sharpness as the most important quality metric 1 , preferably specified as a number between [0, 1] expressing how close the achieved resolution is to the theoretical resolution of the system. SAS images are formed by coherently combining echoes gathered over an interval of acoustic frequencies and aspect angles. The standard approach for estimating resolution in an image is to calculate the -3 dB width of candidate point scatterers, but there are various methods on how to find suitable candidate scatterers 2–5. Point scatterers must be distinguished from larger targets as well as speckle in order to be able to use them to correctly estimate the image resolution. Large targets are undesirable because their width in space is much larger than the system resolution. Speckle is also undesirable because the width of the peaks in speckle are always the same size, independent of the focus quality of the system. In this paper, for detecting candidate point scatterers in SAS images, we explore the suitability of a wavelet shrinkage method6 using two non-overlapping looks obtained from a full resolution single look complex (SLC) image. We have applied this method in earlier work 3 and will here focus on a complete description and explanation of how and why it works. The idea is that for point scatterers, the coherence between looks is high, while speckle and noise are uncorrelated. Focusing on the relevant scales and orientations, our approach can enhance point scatterers and de-emphasize texture, thereby facilitating the detection of point scatterers for resolution estimation. The remainder of this paper is as follows. In Section 2 we give an introduction to the wavelet shrinkage approach. Section 3 interprets the method in light of its application of detection of point scatterers, followed by Section 4 highlighting details on our choices for the implementation. Results for simulated and real SAS data are shown in Section 5. The paper concludes with a summary and outlook over future work in Section 6.