Hyperspectral Classification of Hazardous Materials Based on Deep Learning

Yanlong Sun (), Jinxing Hu, Diping Yuan, Yaowen Chen, Yangyang Liu, Qi Zhang and Wenjiang Chen
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Yanlong Sun: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Jinxing Hu: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Diping Yuan: Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518046, China
Yaowen Chen: School of Electronic & information Engineering, Chongqing Three Gorges University, Chongqing 404121, China
Yangyang Liu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Qi Zhang: College of Safety and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Wenjiang Chen: Shenzhen Urban Public Safety and Technology Institute, Shenzhen 518046, China

Sustainability, 2023, vol. 15, issue 9, 1-18

Abstract: The identification of hazardous materials is a key measure in the prevention and control of fire and explosion disasters. Conventional techniques used to identify hazardous materials include contact detection and post-sampling laboratory testing, which cannot meet the needs of extreme environments, where personnel and equipment are not accessible for on-site detection. To address this problem, this paper proposes a method for the classification and identification of hazardous materials based on convolutional neural networks, which can achieve non-contact remote detection of hazardous materials. Firstly, a dataset containing 1800 hyperspectral images of hazardous materials, which can be used for deep learning, is constructed based on the hazardous materials hyperspectral data cube. Secondly, based on this, an improved ResNet50-based classification method for hazardous materials is proposed, which innovatively utilizes a classification network based on offset sampling convolution and split context-gated convolution. The results show that the method can achieve 93.9% classification accuracy for hazardous materials, which is 1% better than the classification accuracy of the original ResNet50 network. The network also has high performance under small data volume conditions, effectively solving the problem of low classification accuracy due to small data volume and blurred image data features of labelled hazardous material images. In addition, it was found that offset sampling convolution and split context-gated convolution showed synergistic effects in improving the performance of the network.

Keywords: hazardous materials; hyperspectral classification; split context-gated convolution; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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