基于高光谱图像的矿物种类深度识别方法

Deep identification method of mineral species based on hyperspectral images

矿物识别较大程度上依赖人工经验判断,这种方法成本高昂且受矿物识别人主观影响较大,文中提出利用深度学习神经网络的方法自动识别矿物种类。通过模拟专家目视识别矿物的方式采集了矿物RGB图像和高光谱图像样本,利用以上样本对卷积神经网络进行训练并得到矿物种类识别模型。实验分析结果表明:矿物RGB图像包含的信息较单一,不足以区分矿物种类,识别效果较差,识别准确率仅约39.52%;矿物高光谱图像所含信息更为丰富,能有效表达矿物种类特征,因此识别表现优异,模型识别准确率超过94.7%,能满足实际的生产需求。

Because mineral identification depends on labor to a large extent,it is of high costs and greatly affected by the subjective influence of mineral-identifying people.In this paper,mineral RGB images and hyperspectral image samples were collected by simulating the way how experts visually identify minerals.With the above samples,the convolutional neural network was trained and the mineral type identification model obtained.The experimental results show that due to the relatively single information contained in the mineral RGB image,it is not enough to distinguish mineral types,the recognition effect is poor,with the recognition accuracy of only approximately 39.52%.However,the hyperspectral images of minerals contain more abundant information and can effectively express the characteristics of mineral types.Therefore,their recognition performance is excellent,with the model recognition accuracy of more than 94.7%,which can meet the actual production needs.