Hierarchical Deep Feature Representation for High-Resolution Scene Classification

High-resolution scene classification is a fundamental yet challenging problem due to rich image variations in viewpoint, object pose and spatial resolution, etc, which results in large within-class diversity and high between-class similarity. In the paper we focus on tackling the problem of how to learn appropriate feature representation for high-resolution scene classification. To achieve better scene representation, we proposed a combined CNN feature learning framework in multi-scale multi-layer based Gaussian coding (mSmL-Gcoding) manner. In addition, a novel feature coding with Gaussian descriptor is introduced to enhance the discriminative ability of CNN features. Experimental results on two publicly available challenging scene datasets validated that the effectiveness of our method and found it compared favorably with state-of-the-arts.