Classification of scenes across multi-sensor remote sensing images with different spatial, spectral, temporal resolutions involves identification of variable length spatial patterns of objects in a scene. So, it necessitates the use of local representations from different regions of a scene in order to comprehend the scene formation. In this paper, we propose a dynamic kernel based representation to handle the patterns of variable lengths in the scenes of remote sensing images. These kernels help to assimilate spatial variability captured using convolutional features in a Gaussian mixture model. The statistics of GMM facilitate the dynamic kernels in preserving the local spatial similarities while handling the changes in spatial content globally within the same scene. The efficacy of the proposed method using two variants of the dynamic kernels is demonstrated on three benchmark scene classification datasets, namely, UCM Land Use (21 classes), Aerial image dataset (30 classes), and NWPU-RESISC45 (45 classes). Our experiments show that the mean interval kernel is better discriminative as it makes use of first and second-order statistics of GMM. © 2021 IEEE.