The invention discloses a remote sensing image change detection method using a low-rank prior learning discrimination dictionary, which mainly improves the accuracy of unsupervised remote sensing image change detection and replaces automatic threshold selection with learning of a change discrimination dictionary and a non-change discrimination dictionary. The method comprises the following steps:(1) respectively inputting two multi-temporal remote sensing images to be detected; (2) performing super-pixel segmentation on the difference image to obtain a super-pixel total sample; (3) learning aglobal dictionary and a sparse coefficient for the superpixel total sample; (4) selecting samples according to low-rank representation of the sparse coefficient matrix; (5) respectively learning thedictionaries by using the changed samples and the invariable samples; and (6) calculating a reconstruction error of each pixel point neighborhood block under the discrimination dictionary, and comparing the reconstruction errors to obtain a change region binary image. Change samples and unchanged samples can be selected in an unsupervised manner for change detection, and the method can be appliedto the remote sensing fields of disaster assessment, urban expansion detection, land coverage, utilization detection and the like.