CAJ | 학술논문

提出了基于高光谱图像技术的庐山云雾茶产地鉴别方法。利用高光谱成像系统采集地理标志庐山云雾茶和广西、四川、福建3个其他产地云雾茶的高光谱数据，采用主成分分析法从原始高光谱数据块中选取3个特征波长：792．20，831．47，870．97 nm，分别提取每个特征波长下的灰度图像的纹理特征，利用BP神经网络方法建立庐山云雾茶产地鉴别模型。模型训练时的回判识别率为97．25％，预测时的识别率为95％，说明利用高光谱图像技术追溯庐山云雾茶产地可行。
제출료기우고광보도상기술적려산운무다산지감별방법。이용고광보성상계통채집지리표지려산운무다화엄서、사천、복건3개기타산지운무다적고광보수거，채용주성분분석법종원시고광보수거괴중선취3개특정파장：792．20，831．47，870．97 nm，분별제취매개특정파장하적회도도상적문리특정，이용BP신경망락방법건립려산운무다산지감별모형。모형훈련시적회판식별솔위97．25％，예측시적식별솔위95％，설명이용고광보도상기술추소려산운무다산지가행。
A rapid method was developed for discrimination of the geographical origins of Lushan mist tea by hyper-spectral imaging technique .The mist tea with Lushan geographic signs and mist tea from other places ( Guangxi ,Sichuan and Fujian ) were investigated .A hyper-spectral imaging system was used to collect hyper-spectral image data of the tea .Three feature wavelengths namely ,792.20,831.47 and 870.97 nm were opti-mized by principal component analysis method from the raw hyper-spectral images .The texture features were extracted from each gray image of each characteristic wavelength .BP-ANN model was used to develop a dis-crimination model.The correct discrimination rate of the model was 97.5%in the training set,and the discrimi-nation rate of model was 95%in the prediction set .The overall results sufficiently demonstrated that hyper-spectral imaging technology could be used to identify rapidly the geographical origins of mist tea .