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首頁> 外文會(huì)議>Chemical and Biological Standoff Detection II >Analysis of the behaviour of a neural network model in the identification and quantification of hyperspectral signatures applied to the determination of water quality
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Analysis of the behaviour of a neural network model in the identification and quantification of hyperspectral signatures applied to the determination of water quality

機(jī)譯：神經(jīng)網(wǎng)絡(luò)模型在高光譜特征識(shí)別和定量中的行為分析，用于水質(zhì)測(cè)定

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In this work an Unsupervised Neural Computing Model formed by two neural networks is presented: a Self-Organizing Map (SOM) Network and a Hopfield Recurrent Neural Network (HRNN). The first network extracts the endmembers found in the image, analyzing each pixel, and the second network gets the endmember abundances for each pixel in the image. One of the application fields of the proposed methodology is the water quality analysis. In order to study the behaviour of the proposed model, simulation methods have been used to generate hyperspectral signatures from the water spectra obtained in the laboratory. Such data are used for the training and testing of the network. The first subnetwork extracts, from the datasets, the endmembers that are used as training patterns in the second one, that provides the matching abundances. The results obtained here will be applied to the treatment of the hyperspectral image Caceres ES-4, got by the sensors DAIS and ROSIS, from Guadiloba reservoir.
機(jī)譯：在這項(xiàng)工作中，提出了由兩個(gè)神經(jīng)網(wǎng)絡(luò)組成的無監(jiān)督神經(jīng)計(jì)算模型：自組織映射（SOM）網(wǎng)絡(luò)和Hopfield遞歸神經(jīng)網(wǎng)絡(luò)（HRNN）。第一個(gè)網(wǎng)絡(luò)提取圖像中發(fā)現(xiàn)的端成員，分析每個(gè)像素，第二個(gè)網(wǎng)絡(luò)獲取圖像中每個(gè)像素的端成員豐度。所提出方法的應(yīng)用領(lǐng)域之一是水質(zhì)分析。為了研究所提出模型的行為，已使用模擬方法從實(shí)驗(yàn)室獲得的水光譜中生成高光譜特征。此類數(shù)據(jù)用于網(wǎng)絡(luò)的訓(xùn)練和測(cè)試。第一個(gè)子網(wǎng)從數(shù)據(jù)集中提取用作第二個(gè)訓(xùn)練模式中的訓(xùn)練模式的端成員，后者提供匹配的豐度。此處獲得的結(jié)果將用于處理由Guadiloba水庫的DAIS和ROSIS傳感器獲得的高光譜圖像Caceres ES-4。

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