CNN coal and rock recognition method based on hyperspectral data
Jianjian Yang1
Boshen Chang2
Yuchen Zhang2
Wenjie Luo2
Shirong Ge1
Miao Wu1
1.School of Mechatronics and Information Engineering,China University of Mining and Technology-Beijing,Beijing 100083,China;Key Laboratory of Intelligent Mining and Robotics,Ministry of Emergency Management,Beijing,China2.School of Mechatronics and Information Engineering,China University of Mining and Technology-Beijing,Beijing 100083,China
摘要：Aiming at the problem of coal gangue identification in the current fully mechanized mining face and coal washing,this article proposed a convolution neural network(CNN)coal and rock identification method based on hyperspectral data.First,coal and rock spectrum data were collected by a near-infrared spectrometer,and then four methods were used to filter 120 sets of collected data:first-order differential(FD),second-order differential(SD),standard normal variable transformation(SNV),and multi-style smoothing.The coal and rock reflectance spectrum data were pre-processed to enhance the intensity of spec-tral reflectance and absorption characteristics,as well as effectively remove the spectral curve noise generated by instrument performance and environmental factors.A CNN model was constructed,and its advantages and disadvantages were judged based on the accuracy of the three parameter combinations(i.e.,the learning rate,the number of feature extraction layers,and the dropout rate)to generate the best CNN classifier for the hyperspectral data for rock recognition.The experiments show that the recognition accuracy of the one-dimensional CNN model proposed in this paper reaches 94.6％.Verification of the advantages and effectiveness of the method were proposed in this article.
机标关键词：methoddatarockbasedcoalhyperspectralrecognition
论文发表日期：2022-10-28
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：12( 59-70 )
英文信息