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Study on bloody clam population identification based on multi-spectral image

机译：基于多光谱图像的血腥蛤蜊种群识别研究

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An identification method was proposed for distinguishing the bloody clams from different populations based on multi-spectral image technology. Three populations of bloody clams were collected from Korea, Shandong, and Zhejiang, respectively. The multi-spectral images of bloody clam shells were acquired by CIR MS3100 multi-spectral camera. The graylevel co-occurrence matrixes (GLCM) of the three sub-images were calculated and the texture features of images were calculated according to the GLCM. 3 ratios were extracted after standard deviation filtering and threshold segmentation for each image. Totally, 15 features were obtained for one sample. 3 principal components were selected by using principal component analysis (PCA). Discriminant analysis and least square-support vector machine (LS-SVM) were used to establish the discrimination models. The prediction accuracy of discriminant analysis and LS-SVM were 64.44% and 46.67%, respectively. The prediction accuracy was low, but it provided a new approach for nondestructive identification of bloody clams from different populations.
机译：提出了一种基于多光谱图像技术的不同群体区分血腥蛤的识别方法。从韩国，山东和浙江收集了三个血腥蛤蜊。 CIR MS3100多光谱相机获取血腥蛤壳的多光谱图像。计算三个子图像的GrayleVel共发生矩阵（GLCM），并根据GLCM计算图像的纹理特征。在标准偏差滤波和每个图像的阈值分割后提取3个比率。完全，获得一个样品的15个特征。通过使用主成分分析（PCA）选择3个主成分。判别分析和最小二乘支持向量机（LS-SVM）用于建立鉴别模型。判别分析和LS-SVM的预测精度分别为64.44％和46.67％。预测精度低，但它为来自不同群体的血腥蛤蜊无损鉴定的新方法。

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