Side scan sonar image resolution and automatic object detection, classification and identification

Abstract

Operational requirements for naval applications have shifted towards the fast, reliable detection and avoidance or elimination of underwater threats (e.g. mines, IEDs (improvised explosive devices),...) over the last decade. For these purposes the ability to reliable separate mines or IEDs from rocks or bottom features is essential. This separation can be much more difficult for IEDs compared to traditional cylindrical or spherical mines. Furthermore, automatic target recognition (ATR) approaches are gaining more and more importance for autonomous UUVs. Since no operator is in the loop, these systems are harmed by a limited number of missed detections or a significant number of false targets. In this context the ability to automatically detect and classify objects depends directly on the true resolution of the acoustic imaging system. All this points towards the need for a high resolution sensor for reliable object detection, classification and identification. Starting with some examples, this paper presents theoretical considerations about the required resolution for the detection, classification and identification process of objects in side scan sonar images. Clues for the required resolution can be directly derived from the Johnson-Criterion for electro-optics systems. Secondly, an image processing software for automatic object detection and classification currently under development at FWG with the assistance of FU-Berlin and FGAN-FOM is presented. This part focuses on an overview of the system and recently developed and tested algorithms. Before applying different detection algorithms, the side scan sonar images are preprocessed including normalization, height estimation plus slant range correction and geo-referencing. Different normalization algorithms can be used. Currently six different screening algorithms for detecting regions of interests (ROIs) with objects of interest are implemented. These screening algorithms base on statistical features within a sliding window, a highlight / shadow analysis after threshold segmentation, a normalized Id-cross correlation with a template, a modified maximally stable extremal regions (MSER) approach, a k-means and a higher order statistic based segmentation. Afterwards false detections of ROIs without objects of interest are eliminated by applying a single snake algorithm for the entire highlight and shadow area, a coupled snake algorithms for the highlight area and for the shadow area, a 2D-cross correlation with reference images of MLOs and an iterative segmentation, all combined with robust and fast classifiers. The final processing step is a classifier (Probabilistic Neural Network (PNN)). Also a simple data fusion strategy was tested based on the output of the different screening and reduction of false positives algorithms. Finally, consequences for image processing with improved sensor resolution are discussed. All algorithms were tested using a data set representing roughly 25 km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the sea floor. This data set was in part collected by the SeaOtter MK I AUV from Atlas Elektronik and gathered in the Baltic Sea and the Mediterranean Sea. Different side scan sonar systems were used.