Title page for etd-0802111-101723


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URN etd-0802111-101723
Author Ying-Zhang Li
Author's Email Address m965040005@student.nsysu.edu.tw
Statistics This thesis had been viewed 5360 times. Download 694 times.
Department Marine Environment and Engineering
Year 2010
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Automatic Recognition of Artificial Objects in Side-scan Sonar Imerage
Date of Defense 2011-07-15
Page Count 201
Keyword
  • side-scan sonar
  • grey level co-occurrence matrix
  • characteristic function
  • mean
  • entropy
  • homogeneity
  • Bayesian classification
  • Abstract Abstract
    The interpretation and identification of information from the side-scan sonar imagery are mainly depended on visual observation and personal experiences. Recent studies tended to increase the identification efficiency by using numerical analysis methods. This can reduce the error that cause by the differences of observer’s experience as well as by extended time observation. The position around the center line of the slant range corrected side-scan sonar imagery might result in the degradation of the ability of numerical methods to successfully detect artificial objects. Theoretically, this problem could be solved by using a specific characteristic function to identify the existence of concrete reefs, and then filtering the noise of the central line area with a threshold value. This study was intended to develop fully automatic sonar imagery processing system for the identification of cubic concrete and cross-type protective artificial reefs in Taiwan offshore area.
    The procedures of the automatic sonar imagery processing system are as follows:
    (1) Image Acquisition:500kHz with slant range of 75m.
    (2) Feature Extraction:grey level co-occurrence matrix (i.e., Entropy, Homogeneity and Mean)
    (3) Classification:unsupervised Bayesian classifier.
    (4) Object Identification:by characteristic feature (i.e., Entropy).
    (5) Object’s Status Analysis:object’s circumference、area、center of mass and quantity.
      This study used the sonar images collected at Chey-Ding artificial reef site in Kaohsiung City as a case study, aiming to verify the automatic sonar imagery processing system and find out the optimum window size. The image characteristic functions include one set of first order parameter (i.e., mean) and two sets of second order parameter (i.e., entropy and homogeneity). Eight(8) sonar images with 1-8 sets of cubic concrete and cross-type protective artificial reefs where used in this step. The identification efficiency of the system, in terms of the produce’s accuracy, is 79.41%. The results illustrated that there were 16~28 sets of artificial reefs being detected in this case which is comparable with the actual amount of 17 sets. Based on this investigation, the optimum window size was concluded to be 12×12 pixels with sliding size of 4 pixel.
    Imagery collected at Fang-Liau artificial reef site of Pingtung County was tested. For the purpose of applicability, the original imagery (2048×2800 pixels) was divided into 8 consecutive smaller sized imagery frames(2048×350 pixels). The influence of using a two-fold classification procedure and a central filtering method to reduce the noise that caused by slant range correction were discussed. The results showed that central line filtering method is applicable. The results of object’s status analysis showed that there are 156-236 sets of reefs existed. Automatic determination of the target using the characteristic function of entropy is feasible. If the value is larger than 1.45, it represents positive identification of concrete artificial reefs. It can be classified as muddy sand seabed type if the value is smaller than 1.35. If the value is between 1.35~1.45, it illustrates the existence of a transition zone where objects of smaller in dimensions might exist.
    To achieve the purpose of automatic operation, firstly, we have to identify the existence of the concrete reefs by using the specific characteristic function. Based on the result of existing concrete reefs, suture line filtering method will hence be used to filter the noise from the image information. For that all of the procedures are automatically operated without human intervention.
    Key word: side-scan sonar ; characteristic function ; gray level co-occurrence matrix ; Bayesian classification ;entropy ; homogeneity ; mean
    Advisory Committee
  • Min-Chang Wang - chair
  • YI-Ching Chen - co-chair
  • Huo-Wang Chen - co-chair
  • Dung-Jie Li - co-chair
  • Wen-Miin Tian - advisor
  • Files
  • etd-0802111-101723.pdf
  • Indicate in-campus at 2 year and off-campus access at 2 year.
    Date of Submission 2011-08-02

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