Mine reclamation based on remote sensing information and error compensation extreme learning machine. Issue 2 (7th February 2021)

• Record Type: Journal Article
• Authors: Xiao, Dong; Xie, Hongfei; Fu, Yanhua; Li, Feifei
• Is Part Of: Spectroscopy letters. Volume 54:Issue 2(2021)
• Page Start: 151
• Page End: 164
• Publication Date: 2021-02-07
• Subjects: Copper mine -- extreme learning machine -- land reclamation -- remote sensing; Spectrum analysis -- Periodicals; 543.5
• DOI: 10.1080/00387010.2021.1877727
• Languages: English
• ISSNs: 0038-7010

Abstract

Abstract: In view of the many shortcomings of traditional copper ore detection methods, this paper proposes a new method for copper ore content detection, which is to detect copper ore content through spectral information. And we propose an error compensation extreme learning machine model. Using the error compensation extreme learning machine and the spectral information of the copper ore can quickly and accurately identify the copper content in the copper ore. Finally, the satellite remote sensing data were simulated, and the copper content distribution in the mining area was obtained. This provides guidance for future mining, reclamation and ore prospecting.