Recognition method for high-resolution remote-sensing imageries of ionic rare earth mining based on object-oriented technology

Abstract

Ion-absorbing–type rare earth ore is a precious mineral resource of China. The situation of rare earth mining in rare earth mining areas can be monitored precisely with remote-sensing image object-oriented classification methods. The high spatial resolution of remote-sensing technology provides a solution to this issue. This study developed an object-oriented identification method using high spatial resolution remote-sensing imageries to supervise ionic rare earth mining, beginning with the sedimentation tank stage of the mining process and its spatial aggregation distribution relationship. Then, the method was tested using Pleiades satellite remote-sensing imageries of the Lingbei rare earth mining area in Dingnan County of Ganzhou City and aerial remote-sensing imageries of the Heling rare earth mining area in Xunwu County of Ganzhou City. The results showed that the identification accuracy of the satellite and aerial imageries for sedimentation tanks was 94.17% and 90.03%, respectively. Thus, this method can provide a rapid, accurate, and dynamic technology for monitoring illegal rare earth mining.