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基于堆叠式自动编码器分类器对高光谱图像进行猪肉变质的无损预测
农业监测
2026/09/21
摘要：本研究介绍了一种多时相高光谱图像分析方法的初步结果，该方法用于评估猪肉随时间变质的情况。所提出的方 法成本低廉，且几乎无损。分析高光谱数据，并将相关信息简化为仅三个波长的信息。分析由由两个堆叠式自动编码器 和一个 softmax 输出层组成的二元分类器执行。使用自动编码器将输入空间的维度减少了十倍。所提出的分类器已导致 97.2% 的正确决策，这表明该方法在监测肉类安全方面具有巨大潜力。 索引词：高光谱成像、肉质评估、机器学习、神经网络。
期刊：2019 27th European Signal Processing Conference (EUSIPCO)
DOI：10.23919/EUSIPCO.2019.8903164
设备：GaiaField
Non-Destructive Prediction of Pork Meat Degradation using a Stacked Autoencoder Classifier on Hyperspectral Images
Abstract：This work presents initial results on a multitemporal hyperspectral image analysis method to evaluate the time degra dation of pork meat. The proposed method is inexpensive and practically non-destructive. The hyperspectral data is analyzed and the relevant information is reduced to the information in only three wavelengths. The analysis is performed by a binary classifier composed by two stacked autoencoders and a softmax output layer. The use of autoencoders reduces tenfold the dimension of the input space. The proposed classifier has led to 97.2% of correct decisions, which indicates the great potential of the methodology to monitor the safety of meat.
Keywords：Hyperspectral imaging, meat quality assess ment, machine learning, neural network.
