• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Fisheries Science >Classification of fish schools based on evaluation of acoustic descriptor characteristics
【24h】

Classification of fish schools based on evaluation of acoustic descriptor characteristics

机译：基于声学描述符特征评估的鱼群分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Acoustic surveys were conducted from 2002 to 2006 in the East China Sea off the Japanese coast in order to develop a quantitative classification typology of a pelagic fish community and other co-occurring fishes based on acoustic descriptors. Acoustic data were postprocessed to detect and extract fish aggregations from echograms. Based on the expert visual examination of the echograms, detected schools were divided into three broad fish groups according to their schooling characteristics and ethological properties. Each fish school was described by a set of associated descriptors in order to objectively allocate each echo trace to its fish group. Two methods of supervised classification were employed, the discriminant function analysis (DFA) and the artificial neural network technique (ANN). We evaluated and compared the performance of both methods, which showed encouraging and about equally highly correct classification rates (ANN 87.6%; DFA 85.1%). In both techniques, positional and then morphological parameters were most important in discriminating among fish schools. Fish catch composition from midwater trawling validated the fish group classification through one representative example of each grouping. Both methods provided the essential information required for assessing fish stocks. Similar techniques of fish classification might be applicable to marine ecosystems with high pelagic fish diversity. Keywords Acoustic descriptor - Artificial neural network - Discriminant function analysis - Fish classification - Species identification
机译：从2002年到2006年，在日本沿海的东中国海进行了声学调查，目的是建立基于声学描述符的远洋鱼类群落和其他共生鱼类的定量分类类型。对声音数据进行后处理，以检测并从回波图中提取鱼的聚集体。在对回波图进行专业目视检查的基础上，根据检测到的鱼群的学习特征和文化特性将其分为三大类。每个鱼群都由一组相关的描述符来描述，以便客观地将每个回波轨迹分配给其鱼群。采用监督分类的两种方法，判别函数分析（DFA）和人工神经网络技术（ANN）。我们评估并比较了两种方法的性能，这两种方法均显示出令人鼓舞的和大约同样正确的分类率（ANN 87.6％； DFA 85.1％）。在这两种技术中，位置和形态参数对于区分鱼群最为重要。中水拖网捕捞的鱼群组成通过每个分组的一个代表性示例验证了鱼群的分类。两种方法都提供了评估鱼类种群所需的基本信息。相似的鱼类分类技术可能适用于中上层鱼类多样性高的海洋生态系统。关键词声学描述子人工神经网络判别函数分析鱼类分类物种鉴定

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号