 This paper proposes a new approach to remote sensing image classification based on sparse representation of deep learning features. It uses convolutional neural networks, CNNs, to extract high-level spatial information from images which are then represented as sparse vectors in a lower-dimensional space. This allows for more efficient classification than existing methods such as EMAPs and sparse coding. Experimental results show that the proposed method outperforms these other approaches. This article was authored by Heming Leong and Cheely.