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基于SVM的决策融合鱼类识别方法

DOI: 10.3969/j.issn.1006-7043.201403083

Keywords: 鱼类识别, 多方位, 决策融合, 支持向量机, 小波包变换, 离散余弦变换

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Abstract:

为解决基于声学散射数据的高精度鱼类识别问题, 提出一种基于SVM的多方位声散射数据决策层融合的鱼类识别方法。利用小波包变换(WPT)和离散余弦变换(DCT)方法对多方位声散射数据进行特征提取, 并进行特征降维处理。然后采用SVM分类器对每个方位提取的特征做出多次决策, 并输出最终识别结果。采用3种不同鱼类作为研究对象, 设计了可靠的获取多方位声散射数据的实验方案, 给出不同方位数量条件下, 基于WPT和DCT特征量的识别率。理论分析及实验数据处理结果表明, 随着方位数量的增加, 总体识别率呈升高的趋势, 基于SVM的多方位声散射数据决策层融合方法可以有效提高识别率至90%以上。

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