A deep heterogeneous feature fusion approach for automatic land-use classification

In remote sensing, images have assorted appearances due to fused boundaries, making it difficult to generate discriminative feature representation for the purpose of conducting classification tasks. Therefore, an effective feature representation can boost the classification accuracy in the field of satellite imaging. In this paper, we propose a novel hybrid system for satellite image classification that combines the distinct information of deep features, and generate a discriminative representation by preserving the essential information of original feature space. We use pre-trained convolutional neural networks for extracting our features via transfer learning. For this purpose, we first propose a single strategy where fully connected layers are effectively used to represent different levels of image features. Secondly, a robust approach, called entropy controlled neighborhood component analysis, is then proposed to optimize fusion of multiple layers of different architectures in a unified hierarchical manner. To validate the effectiveness of the proposed approach, we perform experiments on three benchmark satellite datasets; UC MERCED, RS19 and AID. We statistically analyze our results with analysis of variance and post-hoc Bonferroni test, and compare our proposed methodology with state-of-the-art methods. Experimental results show that the proposed methodology can accurately classify satellite images with 99.7%, 99.1% and 92.2% accuracy with selected classifier and by utilizing less than 5% features.