Sea bottom line tracking in side-scan sonar image through the combination of points density clustering and chains seeking

2019 ◽  
Vol 25 (3) ◽  
pp. 849-865 ◽  
Author(s):  
Aixue Wang ◽  
Ian Church ◽  
Jun Gou ◽  
Jianhu Zhao
Keyword(s):  
Bottom Line ◽  
Side Scan Sonar ◽  
Sonar Image ◽  
Sea Bottom ◽  
Density Clustering ◽  
Line Tracking

scholarly journals Bottom Detection Method of Side-Scan Sonar Image for AUV Missions

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Huapeng Yu ◽  
Ziyuan Li ◽  
Dailin Li ◽  
Tongsheng Shen
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Detection Method ◽  
Time Window ◽  
Image Features ◽  
Frequency Noise ◽  
Threshold Method ◽  
Bottom Line ◽  
Side Scan Sonar ◽  
Noise Characteristics ◽  
Sea Bottom

In order to obtain the measurement parameters of the sea bottom geomorphology or underwater objects, the first step in side-scan sonar (SSS) image processing is bottom detection. Due to the complexity of the marine environment, the acoustic signals received by SSS are usually polluted by noises, which affect its image quality and make the extraction of image features difficult. To address this problem, this study proposes an automatic detection method for the sea bottom line based on the actual experimental acquisition of SSS images, which is supposed to support the autonomous underwater vehicle (AUV) for intelligent target detection and classification. The proposed method comprises four main steps. First, the raw SSS data is analyzed to obtain a grayscale image, and the blind zone boundary of the image is obtained using the threshold method. Then, the noise characteristics of the image are analyzed and the denoising algorithm is optimized to effectively remove high-frequency noise. Next, spatial-temporal matching calculations are performed on each ping port and starboard data, and the accurate coordinates of first bottom returns are obtained through extreme value detection. Finally, automatic and accurate detection of the bottom line is realized according to the smooth processing of the coordinate sequence of first bottom returns. The experiments have demonstrated the effectiveness of the proposed method. As the method does not require human intervention in adjusting parameters during operation, the proposed method with a certain time window imposed during image acquisition will be suitable for AUV missions when the SSS is determined.

scholarly journals A new methodology for monitoring P. oceanica meadows in Tavolara Punta Coda Cavallo MPA using GIS

2015 ◽  
Author(s):  
Pieraugusto Panzalis ◽  
Andrea Deiana ◽  
Sarah Caronni ◽  
Augusto Navone
Keyword(s):  
Scale Up ◽  
Continuous Mapping ◽  
Detailed Knowledge ◽  
Regular Grid ◽  
Side Scan Sonar ◽  
Sea Bottom ◽  
The Status ◽  
Square Cells ◽  
Set Up ◽  
The Many

Marine Protected Areas (MPAs) are acknowledged globally as effective tools for the protection and management of the marine environment; however, to get effective results it is necessary to set up a proper and continuous mapping of the marine territory, in order to gain detailed knowledge of its different aspects. Therefore, the implementation and maintenance of a modern GIS (Geographic Information System) has become an indispensable task for the MPA of Tavolara - Punta Coda Cavallo to collect, aggregate, classify, and track the conducted mapping activities. Between 2011 and 2012 the sea bottom of the MPA was surveyed using different methods: by means of a multi-beam echo sounder and of a side scan sonar, as well as conducting fast scientific scuba divings with re-breathers and underwater position system technologies. High resolution geodatasets, characterized by a significantly high quality in representing and describing the sea bottom and its habitats, were produced in both feature (scale up to 1:1.250) and raster formats (up to 30cm/pixel for sonar images and 1m/pixel for bathymetry) and they currently constitute the basis of the MPA's GIS, including its 3D applications and its web map services for desktop and mobile devices (iPhone & Android). To update the above described geodatasets during time, acquiring new data on the conservation targets considered in monitoring activities, among which the status of P. oceanica meadows is of the most important ones, a long term mapping plan was realized on the basis of an innovative methodology elaborated by the MPA considering both the wideness of the area and the limited funds available at present. The whole MPA was divided in territorial units by means of a regular grid of square cells having a 100m side with the logic of starting the mapping activities from the mainly important areas and then to spread the surveys up to fill the whole mosaic. All the new data acquired with this methodology could then be mixed, compared and indexed within the same cell and/or in the many already available geodatases, starting from those dated 2006 having a regular grid with square cells of 500m per side.

scholarly journals Application of Shape From Shading Technique for Side Scan Sonar Images

2013 ◽  
Vol 20 (3) ◽  
pp. 39-44 ◽  
Author(s):  
Krzysztof Bikonis ◽  
Marek Moszynski ◽  
Zbigniew Lubniewski
Keyword(s):  
Digital Signal ◽  
Shape From Shading ◽  
Imaging Systems ◽  
Side Scan Sonar ◽  
Human Eye ◽  
Sea Bottom ◽  
Acoustic Images ◽  
Computer Vision Systems ◽  
Level 2 ◽  
A Current

Abstract Digital signal processing technology has revolutionized a way of processing, visualisation and interpretation of data acquired by underwater systems. Through many years side scan sonars were one of the most widely used imaging systems in the underwater environment. Although they are relatively cheap and easy to deploy, more powerful sensors like multibeam echo sounders and sonars are widely used today and deliver 3D bathymetry of sea bottom terrain. Side scan sonar outputs data usually in a form of grey level 2D acoustic images but the analysis of such pictures performed by human eye allows creating semi-spatial impressions of seafloor relief and morphology. Hence the idea of post-processing the side scan sonar data in a manner similar to human eye to obtain 3D visualisation. In recently developing computer vision systems the shape from shading approach is well recognized technique. Applying it to side scan sonar data is challenging idea used by several authors. In the paper, some further extensions are presented. They rely on processing the backscattering information of each footprint (pixel in sonar image) along with its surroundings. Additionally, a current altitude is estimated from the size of shadow areas. Both techniques allow constructing 3D representation of sea bottom relief or other investigated underwater objects.

scholarly journals Side-scan Sonar Image De-noising Based on Bidimensional Empirical Mode Decomposition and Non-local Means

2020 ◽  
Vol 206 ◽  
pp. 03019
Author(s):  
Kun Zhao ◽  
Jisheng Ding ◽  
YanFei Sun ◽  
ZhiYuan Hu
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Side Scan Sonar ◽  
Sonar Image ◽  
Local Means ◽  
Residual Component ◽  
Mode Decomposition ◽  
Non Local ◽  
Bidimensional Empirical Mode Decomposition

In order to suppress the multiplicative specular noise in side-scan sonar images, a denoising method combining bidimensional empirical mode decomposition and non-local means algorithm is proposed. First, the sonar image is decomposed into intrinsic mode functions(IMF) and residual component, then the high frequency IMF is denoised by non-local mean filtering method, and finally the processed intrinsic mode functions and residual component are reconstructed to obtain the de-noised side-scan sonar image. The paper’s method is compared with the conventional filtering algorithm for experimental quantitative analysis. The results show that this method can suppress the sonar image noise and retain the detailed information of the image, which is beneficial to the later image processing.

DcNet: Dilated Convolutional Neural Networks for Side-Scan Sonar Image Semantic Segmentation

2021 ◽  
Vol 20 (5) ◽  
pp. 1089-1096
Author(s):  
Xiaohong Zhao ◽  
Rixia Qin ◽  
Qilei Zhang ◽  
Fei Yu ◽  
Qi Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Side Scan Sonar ◽  
Sonar Image

Feature Sparse Coding With CoordConv for Side Scan Sonar Image Enhancement

2020 ◽  
pp. 1-5
Author(s):  
Bokyeung Lee ◽  
Bonhwa Ku ◽  
Wanjin Kim ◽  
Seungil Kim ◽  
Hanseok Ko
Keyword(s):  
Image Enhancement ◽  
Sparse Coding ◽  
Side Scan Sonar ◽  
Sonar Image

scholarly journals Side scan sonar image segmentation and feature extraction

OCEANS 2009 ◽  
2009 ◽  
Author(s):  
Miguel Pinto ◽  
Bruno Ferreira ◽  
Anibal Matos ◽  
Nuno Cruz
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Side Scan Sonar ◽  
Sonar Image

scholarly journals On-Line Multi-Class Segmentation of Side-Scan Sonar Imagery Using an Autonomous Underwater Vehicle

2020 ◽  
Vol 8 (8) ◽  
pp. 557 ◽  
Author(s):  
Antoni Burguera ◽  
Francisco Bonin-Font
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Real Data ◽  
Laptop Computer ◽  
Side Scan Sonar ◽  
Sea Bottom ◽  
Acoustic Images ◽  
Computational Speed ◽  
Segmentation Quality ◽  
On Line ◽  
Class Segmentation

This paper proposes a method to perform on-line multi-class segmentation of Side-Scan Sonar acoustic images, thus being able to build a semantic map of the sea bottom usable to search loop candidates in a SLAM context. The proposal follows three main steps. First, the sonar data is pre-processed by means of acoustics based models. Second, the data is segmented thanks to a lightweight Convolutional Neural Network which is fed with acoustic swaths gathered within a temporal window. Third, the segmented swaths are fused into a consistent segmented image. The experiments, performed with real data gathered in coastal areas of Mallorca (Spain), explore all the possible configurations and show the validity of our proposal both in terms of segmentation quality, with per-class precisions and recalls surpassing the 90%, and in terms of computational speed, requiring less than a 7% of CPU time on a standard laptop computer. The fully documented source code, and some trained models and datasets are provided as part of this study.

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