Abstract: The remote sensing images are susceptible to interference factors such as illumination and meteorological conditions,and with the improvement of the resolution of remote sensing equipment,more surface details appear in the remote sensing image.In order to improve the accuracy of remote sensing image scene classification,a self-weighted multi-view metric learning(SW-MVML)method is proposed.Firstly,data features from multiple views are used to learn a discriminative metric space,which makes the similar images compact and dissimilar images as far away as possible.Then,the weight vector is introduced to adaptively adjust the weight relationship among different views.Finally,the kernel technique is used to extend the method to non-linear space,so that the correlation and complementary information hidden between views can be effectively exploited.The experimental results on Google and WHU-RS remote sensing image datasets show that the proposed method achieves good classification performance with the average classification accuracy of 90.26%and 92.62%respectively,which is significantly better than the comparative single-view and multi-view classification methods.