Processing and Analysis of Underwater Acoustic Images Generated by Mechanically Scanned Sonar Systems

2009 ◽  
Vol 58 (7) ◽  
pp. 2061-2071 ◽  
Author(s):  
A. Trucco ◽  
M. Garofalo ◽  
S. Repetto ◽  
G. Vernazza
Keyword(s):  
Underwater Acoustic ◽  
Acoustic Images ◽  
Sonar Systems

scholarly journals Detection of Visual Signatures of Marine Mammals and Fish within Marine Renewable Energy Farms using Multibeam Imaging Sonar

2019 ◽  
Vol 7 (2) ◽  
pp. 22 ◽  
Author(s):  
Francisco Francisco ◽  
Jan Sundberg
Keyword(s):  
Renewable Energy ◽  
Marine Mammals ◽  
Environmental Data ◽  
Marine Renewable Energy ◽  
Marine Energy ◽  
Acoustic Images ◽  
Sonar Systems ◽  
Sonar Images ◽  
Imaging Sonar

Techniques for marine monitoring have been greatly evolved over the past decades, making the acquisition of environmental data safer, more reliable and more efficient. On the other hand, the marine renewable energy sector has introduced dissimilar ways of exploring the oceans. Marine energy is mostly harvested in murky and high energetic places where conventional data acquisition techniques are impractical. This new frontier on marine operations brings the need for finding new techniques for environmental data acquisition, processing and analysis. Modern sonar systems, operating at high frequencies, can acquire detailed images of the underwater environment. Variables such as occurrence, size, class and behavior of a variety of aquatic species of fish, birds, and mammals that coexist within marine energy sites can be monitored using imaging sonar systems. Although sonar images can provide high levels of detail, in most of the cases they are still difficult to decipher. In order to facilitate the classification of targets using sonar images, this study introduces a framework of extracting visual features of marine animals that would serve as unique signatures. The acoustic visibility measure (AVM) is here introduced as technique of identification and classification of targets by comparing the observed size with a standard value. This information can be used to instruct algorithms and protocols in order to automate the identification and classification of underwater targets using imaging sonar systems. Using image processing algorithms embedded in Proviwer4 and FIJI software, this study found that acoustic images can be effectively used to classify cod, harbour and grey seals, and orcas through their size, shape and swimming behavior. The sonar images showed that cod occurred as bright, 0.9 m long, ellipsoidal targets shoaling in groups. Harbour seals occurred as bright torpedo-like fast moving targets, whereas grey seals occurred as bulky-ellipsoidal targets with serpentine movements. Orca or larger marine mammals occurred with relatively low visibility on the acoustic images compared to their body size, which measured between 4 m and 7 m. This framework provide a new window of performing qualitative and quantitative observations of underwater targets, and with further improvements, this method can be useful for environmental studies within marine renewable energy farms and for other purposes.

scholarly journals A Scale-Adaptive Matching Algorithm for Underwater Acoustic and Optical Images

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4226 ◽  
Author(s):  
Jun Liu ◽  
Benyuan Li ◽  
Wenxue Guan ◽  
Shenghua Gong ◽  
Jiaxin Liu ◽  
...  
Keyword(s):  
Estimation Method ◽  
Scale Space ◽  
Optical Data ◽  
Enhancement Effect ◽  
Underwater Acoustic ◽  
Iterative Processing ◽  
Similarity Estimation ◽  
Optical Images ◽  
Acoustic Images ◽  
Scale Detection

Underwater acoustic and optical data fusion has been developed in recent decades. Matching of underwater acoustic and optical images is a fundamental and critical problem in underwater exploration because it usually acts as the key step in many applications, such as target detection, ocean observation, and joint positioning. In this study, a method of matching the same underwater object in acoustic and optical images was designed, consisting of two steps. First, an enhancement step is used to enhance the images and ensure the accuracy of the matching results based on iterative processing and estimate similarity. The acoustic and optical images are first pre-processed with the aim of eliminating the influence of contrast degradation, contour blur, and image noise. A method for image enhancement was designed based on iterative processing. In addition, a new similarity estimation method for acoustic and optical images is also proposed to provide the enhancement effect. Second, a matching step is used to accurately find the corresponding object in the acoustic images that appears in the underwater optical images. In the matching process, a correlation filter is applied to determine the correlation for matching between images. Due to the differences of angle and imaging principle between underwater optical and acoustic images, there may be major differences of size between two images of the same object. In order to eliminate the effect of these differences, we introduce the Gaussian scale-space, which is fused with multi-scale detection to determine the matching results. Therefore, the algorithm is insensitive to scale differences. Extensive experiments demonstrate the effectiveness and accuracy of our proposed method in matching acoustic and optical images.

scholarly journals A Performance Analysis of Feature Extraction Algorithms for Acoustic Image-Based Underwater Navigation

2021 ◽  
Vol 9 (4) ◽  
pp. 361
Author(s):  
António José Oliveira ◽  
Bruno Miguel Ferreira ◽  
Nuno Alexandre Cruz
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Feature Detection ◽  
Acoustic Imaging ◽  
Acoustic Image ◽  
Feature Description ◽  
Underwater Acoustic ◽  
Environmental Features ◽  
Acoustic Images ◽  
Underwater Navigation

In underwater navigation, sonars are useful sensing devices for operation in confined or structured environments, enabling the detection and identification of underwater environmental features through the acquisition of acoustic images. Nonetheless, in these environments, several problems affect their performance, such as background noise and multiple secondary echoes. In recent years, research has been conducted regarding the application of feature extraction algorithms to underwater acoustic images, with the purpose of achieving a robust solution for the detection and matching of environmental features. However, since these algorithms were originally developed for optical image analysis, conclusions in the literature diverge regarding their suitability to acoustic imaging. This article presents a detailed comparison between the SURF (Speeded-Up Robust Features), ORB (Oriented FAST and Rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints), and SURF-Harris algorithms, based on the performance of their feature detection and description procedures, when applied to acoustic data collected by an autonomous underwater vehicle. Several characteristics of the studied algorithms were taken into account, such as feature point distribution, feature detection accuracy, and feature description robustness. A possible adaptation of feature extraction procedures to acoustic imaging is further explored through the implementation of a feature selection module. The performed comparison has also provided evidence that further development of the current feature description methodologies might be required for underwater acoustic image analysis.

Sign in / Sign up

Export Citation Format

Share Document