Multi-deep features fusion for high-resolution remote sensing image scene classification

In view of the small number of categories and the relatively little amount of labeled data, it is challenging to apply the fusion of deep convolution features directly to remote sensing images. To address this issue, we propose a pyramid multi-subset feature fusion method, which can effectively fuse the deep features extracted from different pre-trained convolutional neural networks and integrate the global and local information of the deep features, thereby obtaining stronger discriminative and low-dimensional features. By introducing the idea of weighting the difference between different categories, the weight discriminant correlation analysis method is designed to make it pay more attention to those categories that are not easy to distinguish. In order to mine global and local feature information, the pyramid method is employed to divide feature fusion into several layers. Each layer divides the features into several subsets and then performs feature fusion on the corresponding feature subsets, and the number of subsets from top to bottom gradually increases. Feature fusion at the top of the pyramid obtains a global representation, while feature fusion at the bottom obtains a local detail representation. Our experiment results on three public remote sensing image data sets demonstrate that the proposed multi-deep features fusion method produces improvements over other state-of-the-art deep learning methods.