A semisupervised kernel deformation function, including spatial similarity, is proposed for the classification of remote sensing (RS) images. The method exploits the characteristic of these images, in which spatially nearby points are likely to belong to the same class. To fulfill this assumption, a kernel encoding both spatial and spectral proximity using unlabeled samples is proposed. In this letter, two similarity functions for constructing a spatial kernel are proposed. Experimental tests are performed on very high-resolution multispectral and hyperspectral data. With respect to state-of-the-art semisupervised methods for RS images, the proposed method incorporating spatial similarity obtains higher classification accuracy values and smoother classification maps.