Abstract: In view of the existed complicated problems of the large amount of remote sensing data and complex data processing flow in the process of hyper-spectral remote sensing detection of gold and copper deposit,in order to improve the efficiency of the remote sensing detection of gold and copper deposit,a new remote sensing detection method based on principal component analysis method and spectral angle matching method of gold and copper deposit is proposed.Dimension reduction of the hyper-spectral remote sensing data of gold and copper deposit is conducted by adopting the principal component analysis method;then,the target spectrum is matched by using the spectral angle matching method to realize the goal of the remote sensing detection of gold and copper deposit with high efficiency.Taking the Zijinshan gold and copper deposit as the study example,the samples of gold,copper deposits and surrounding rocks are collected,the experiments of testing the samples with visible-near infrared spectral by using the detection method proposed in this paper is done to dispose the hyper-spectral remote sensing data and identify the gold,copper deposit and surrounding rocks.The experimental results show that the processing flow hyper-spectral remote sensing data of the gold and copper deposit is simplified effectively by using the detection method proposed in this paper,the measured spectral remote sensing data is simplified into 6 main components from 973 bands which can facilitate the flow of computing with little spectral information loss in spectral angle matching,the success rate of the detection method proposed in this paper of the gold and copper deposit is overall reached to 74%，it is further indicated that the detection method has a better applicability in gold and copper deposit in the prospecting work by using hyper-spectral remote sensing technology.

Key words: Gold and copper deposit, Hyper-spectral remote sensing, Principal component analysis, Spectral angle matching, Data dimension reduction, Remote sensing detection