Automatic Detection and Segmentation on Gas Plumes from Multibeam Water Column Images

Jianhu Zhao,Hongmei Zhang,Shiqi Wang,Dongxin Mai

doi:10.3390/rs12183085

Jianhu Zhao, Hongmei Zhang + Show 2 more

Open Access

https://doi.org/10.3390/rs12183085

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Abstract

The detection of gas plumes from multibeam water column (MWC) data is the most direct way to discover gas hydrate reservoirs, but current methods often have low reliability, leading to inefficient detections. Therefore, this paper proposes an automatic method for gas plume detection and segmentation by analyzing the characteristics of gas plumes in MWC images. This method is based on the AdaBoost cascade classifier, combining the Haar-like feature and Local Binary Patterns (LBP) feature. After obtaining the detected result from the above algorithm, a target localization algorithm, based on a histogram similarity calculation, is given to exactly localize the detected target boxes, by considering the differences in gas plume and background noise in the backscatter strength. On this basis, a real-time segmentation method is put forward to get the size of the detected gas plumes, by integration of the image intersection and subtraction operation. Through the shallow-water and deep-water experiment verification, the detection accuracy of this method reaches 95.8%, the precision reaches 99.35% and the recall rate reaches 82.7%. Integrated with principles and experiments, the performance of the proposed method is analyzed and discussed, and finally some conclusions are drawn.

Highlights

Multibeam water column (MWC) data is the product of multibeam echo sounders (MBESs) [1,2]
The Receiver Operating Characteristic (ROC) curve and Area under the Curve (AUC) are often used to evaluate a binary classifier. It mainly shows a trade-off between true positive rate (TPR) and false positive rate (FPR)
This paper proposes a gas plume detection and segmentation method, which can achieve the detection, localization and segmentation of gas plumes from a whole multibeam water column (MWC) image

Summary

Introduction

Multibeam water column (MWC) data is the product of multibeam echo sounders (MBESs) [1,2]. Reemt oatle.S[e1ns7.]20u2s0,e1d2,M308W5 C images to study the temporal and spatial variation of a known ga2s-olfe2a5king area. These studies suggest that using MWC images is an effective way to detect gas plumes. The targets segmented from the MWC images. Background Differential Segmentation The algorithm mentioned above is able to detect and locate the target quickly and correctly. AsfosullmoBwBapsaitenisodegndoasnsoastnruhemetghpaivebtieooanvnb:esoeavmreeisegsmiivoiensnsmi:oondemsoadnedschaanrdacctehrairsaticctseroifsntiocsiseoifnnthoeisMe WinCthime aMgeW, tCheifmolalogwe,intghe TThheewwataetrerenenvivriornonmmenent tisisstsatbalbeleininsesveevrearlalddozoeznens sofofcocnotnitniunuouous sppininggs,s,ananddththereerfeoforereththee babcakcgkrgoruoundndnoniosieseofotfwtwooadajdajcaecnetnot dod/de/veevnenpipnignsgcsacnanbebeasassusmumededtotobebenenaeralyrlythtehseasmame.e. TThheennoiosieseininthtehesusbu-bs-escetcotrosrswwithiththtehesasmame etrtarnasnmsmisissisoinonfrferqeuqeunecnycyisiaspapprporxoixmimataetleylythtehseasmame.e. asfosullmoBwBapsaitenisodegndoasnsoastnruhemetghpaivebtieooanvnb:esoeavmreeisegsmiivoiensnsmi:oondemsoadnedschaanrdacctehrairsaticctseroifsntiocsiseoifnnthoeisMe WinCthime aMgeW, tCheifmolalogwe,intghe TThheewwataetrerenenvivriornonmmenent tisisstsatbalbeleininsesveevrearlalddozoeznens sofofcocnotnitniunuouous sppininggs,s,ananddththereerfeoforereththee babcakcgkrgoruoundndnoniosieseofotfwtwooadajdajcaecnetnot dod/de/veevnenpipnignsgcsacnanbebeasassusmumededtotobebenenaeralyrlythtehseasmame.e. The threshold, TdB, is a key parameter to diagnose the echoes at the same position of the neighbor MWC images to be background noise or not.

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Conclusion