UNSUPERVISED FEATURE CODING ON LOCAL PATCH MANIFOLD FOR SATELLITE IMAGE SCENE CLASSIFICATION

This paper presents an improved unsupervised feature learning (UFL) pipeline to discover intrinsic structures of local image patches as well as learn good feature representations automatically for image scenes. In our method, the original image patch vectors embedded in the high-dimensional pixel space are first mapped into a low-dimensional intrinsic space by linear manifold techniques, and then k-means clustering is performed on the patch manifold to learn a dictionary for feature encoding. To generate the feature representation for each local patch, triangle encoding method is applied with the learned dictionary on the same patch manifold. Finally, the holistic scene representations are obtained via the bagof-visual-words (BOW) framework. We apply the proposed method on an aerial scene dataset. Experiments on the dataset show very promising results and demonstrate that our UFL pipeline can generate very effective local features for image scenes.