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基于SVM-DS多特征融合的雜草識(shí)別
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國(guó)家自然科學(xué)基金資助項(xiàng)目(60975007、31101075)


Weed Recognition Based on SVM-DS Multi-feature Fusion
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    摘要:

    為解決單一特征識(shí)別雜草的低準(zhǔn)確率和低穩(wěn)定性,提出一種支持向量機(jī)(SVM)和DS(Shafer-Dempster)證據(jù)理論相結(jié)合的多特征融合雜草識(shí)別方法。在對(duì)田間植物圖像處理的基礎(chǔ)上,提取植物葉片形狀、紋理及分形維數(shù)3類(lèi)特征,分別以3類(lèi)單特征的SVM分類(lèi)結(jié)果作為獨(dú)立證據(jù)構(gòu)造基本概率指派(BPA),引入基于矩陣分析的DS融合算法簡(jiǎn)化決策級(jí)融合算法復(fù)雜度,根據(jù)融合結(jié)果及分類(lèi)判決門(mén)限給出最終的識(shí)別結(jié)果。實(shí)驗(yàn)結(jié)果表明,多特征決策融合識(shí)別方法正確識(shí)別率達(dá)到96.11%,與單特征識(shí)別相比有更好的穩(wěn)定性和更高的識(shí)別率。

    Abstract:

    To address the low accuracy and low stability of a single feature for weed recognition, a multi-feature fusion method based on support vector machine (SVM) and DS (Shafer-Dempster) evidence theory was proposed. Firstly, three types of plant leaf features such as shape, texture and fractal dimension were extracted from the plant leaves after a series of image processing. Then the SVM classification results of each single feature were used as evidences to construct the basic probability assigned (BPA), and the method of DS fusion based on matrix analysis was used for decision fusion. Finally, recognition results were given based on fusion results and classification thresholds. The experimental results showed that the accuracy of multi-feature fusion method was 96。11% which has good performance on accuracy and stability compared with the single feature method in weed recognition.

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何東健,喬永亮,李攀,高瞻,李海洋,唐晶磊.基于SVM-DS多特征融合的雜草識(shí)別[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2013,44(2):182-187. He Dongjian, Qiao Yongliang, Li Pan, Gao Zhan, Li Haiyang, Tang Jinglei. Weed Recognition Based on SVM-DS Multi-feature Fusion[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(2):182-187.

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  • 在線(xiàn)發(fā)布日期: 2013-02-04
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