Paper Info
Title
An Automatic Alarm Method For Buried Targets Based On Membership Classification
Abstract
Because there are many false targets in the seabed imaging of SAS (synthetic aperture sonar), it is difficult for the automatic alarm of buried column targets. An automatic alarm method for buried targets based on membership classification is proposed in this paper. Firstly, the mean-standard deviation maximum entropy segmentation is used to segment seabed image. Then the area and posture ratio of the segmented target are extracted, and the column membership is calculated. Finally, the automatic alarm of column target is realized through column membership discrimination. The real data processing results show that the method can effectively realize the automatic alarm of buried column targets in SAS images.
Year
DOI
Venue
2017
10.1109/ICSPCC.2017.8242496
2017 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING (ICSPCC)
Keywords
DocType
Citations 
SAS image, buried column targets, mean- standard deviation maximum entropy, membership, automatic alarm
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Huanhuan Xue100.68
Weihua Cong200.68
Bibo Zhu300.68