Remote Sensing Scene Classification Based on Multi-Structure Deep Features Fusion
Wei Xue, Xiangyang Dai, Li Liu
Abstract
Convolutional neural networks (CNNs) have been widely used in remote sensing scene classification due to their excellent performance in natural image classification. However, the complementarity of features extracted by different CNNs is seldom exploited, which limits the further improvement of classification accuracy. To solve this problem, we propose a classification method based on multi-structure deep features fusion (MSDFF). First, a data augmentation method based on random-scale cropping is adopted to achieve the expansion of limited data. Then, three popular CNNs are respectively used as feature extractors to capture deep features from the image. Finally, a deep feature fusion network is adopted to fuse these features and implement the classification. The effectiveness of the proposed method is verified on UC Merced, AID, and NWPU-RESISC45 datasets. The proposed method can achieve better performance than state-of-the-art scene classification methods.