The invention discloses a remote sensing image change detection method based on controllable kernel regression and superpixel segmentation. The problems that only grey information of an image is considered when a difference chart is structured, other feature information is underused, k-means clustering is directly carried out on the difference chart, and therefore the situation that a weak declension area cannot be detected is easily caused are mainly solved. The method comprises the steps of adopting the controllable kernel regression on two input time phase images to respectively extract structural feature matrixes, combining feature matrixes of neighbourhoods with the structural feature matrixes respectively, obtaining a local structural feature matrix, decomposing the local structural feature matrix by using a non-negative matrix factorization algorithm, carrying out a difference chart structure on an obtained coefficient matrix, finally segmenting the difference chart to obtain an over-segmentation image by using a superpixel segmentation method, carrying out the K-means clustering on the over-segmentation image, and obtaining a change detection result. The remote sensing image change detection method based on the controllable kernel regression and the superpixel segmentation can keep marginal information of images, is good in noise proof performance, improves change detection precision, and can be applied to fields of disaster situation monitoring, land utilization, agricultural investigation and the like.