Underwater target detection in synthetic aperture sonar data
Underwater target detection in synthetic aperture sonar data
复制标题
合成孔径声纳数据中的水下目标检测
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
D. Bull
中科院分区：
文献类型：
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作者：
P. Hill;A. Achim;D. Bull
从声纳回波中探测水雷等水下目标是一项困难的任务，海底背景复杂多变，使这一任务变得更加复杂。所开发的系统采用了经典的训练和分类结构，给出了典型目标类型的背景与领域知识的统计特征。已为三个给定的海底测试区域制作了一套地面实况标签，其中包含一系列目标类型。该方法使用对数Gabor，匹配和成形滤波器与支持向量机（SVM）分类器一起识别目标的中心。主观测试使我们的自动检测方法与专家操作员的性能进行比较。自动目标检测方法被发现提供性能至少与人类操作员相同的数据（基于一个小的操作员数据集）。（5页）
The detection of underwater targets, such as mines, from sonar returns is a difficult task which is compounded by the complex and variable backgrounds found on the seabed. The developed system employs a classical training and classification structure giving a statistical characterisation of the background together with domain knowledge of typical target types. A set of ground truth labels have been produced for three given seabed test regions which contain a range of target types. The method identifies the centre of targets using log-Gabor, matched and shaped filters together with a Support Vector Machine (SVM) classifier. Subjective testing enabled the comparison of our automatic detection methods with the performance of expert operators. The automatic target detection method was found to offer performance at least as good as human operators on identical data (based on a small operator data set). (5 pages)