||The advantages that a side-scan sonar can offer include large-scale survey areas and high-resolution imagery which can provide the detection and positioning of underwater targets effectively. The purpose of image analysis, classification and positioning in this research was presented by the development of an automated recognition and classification system based on sonographs collected off Kenting area. Major components of the system include gray level co-occurrence matrix method, Baysian classification and cluster analysis.|
The sonograph classified by the automated recognition and classification system was split into two stages. The first stage divided the seafloor into three categories:
(1) Rocky seafloor.
(2) Sandy seafloor.
(3) Acoustic shadow seafloor.
Based on the characteristics of the rocky seafloor, the rocky seafloor was subdivided into five types in the second stage:
(1) Flank reef and small independent reef.
(2) Smooth reef.
(3) Small coral on reef.
(4) Coral on independent reef.
(5) Large coral on reef.
Analysis and proof of the system was conducted by underwater photographs collected off Kenting area in August 4, to 6, 2004. The identification accuracy of the first stage can reach 93% in Shiniuzai area. The characteristic features selected in this research (i.e., entropy and homogeneity) for the classification of various coral reef seafloors was proved adequate and the results was described in map within a Geographic Information System in the second stage.
The results of this research illustrated that the rocky area identified in Shiniuzai was 98,863 m2. Due to image resolution restrictions, only 62,199 m2 of the total rocky area could be defined and classified properly. Among them, the flank reef and small independent reef covered an area of 15,954 m2 (26.3%); the smooth reef covered 3,133 m2 (5.0%); the small coral on reef covered 8,021 m2 (12.8%); the coral on independent reef covered 25,504 m2 (40.7%) and the large coral on reef covered 9,587 m2 (15.3%).
Key words:side scan sonar,coral reef,gray level co-occurrence matrix