HDTFF-Net: Hierarchical Deep Texture Features Fusion Network for High-Resolution Remote Sensing Scene Classification
Wanying Song, Yifan Cong, Shiru Zhang, Yan Wu, Peng Zhang
Abstract
Fusing features from different feature descriptors or different convolutional layers can improve the understanding of scene and enhance the classification accuracy. In this article, we propose a hierarchical deep texture feature fusion network, abbreviated as HDTFF-Net, aiming to improve the classification accuracy of high-resolution remote sensing scene classification. The proposed HDTFF-Net can effectively combine the shallow texture information from manual features and the deep texture information by convolutional neural networks (CNNs). First, for deeply excavating the multiscale and multidirectional shallow texture features in images, an improved Wavelet feature extraction module and a Gabor feature extraction module are designed by fully fusing the structural features into the backbone neural network. Then, to make the output texture features more discriminative and interpretative, we incorporate the above texture feature extraction modules into traditional CNNs (Tra-CNNs), and design two improved deep networks, namely Wave-CNN and Gabor-CNN. Finally, according to the Dempster-Shafer evidence theory, the designed Wave-CNN and Gabor-CNN are fused with the Tra-CNN by a decision-level fusion strategy, which can effectively capture the deep texture features by different feature descriptors and improve the classification performance. Experiments on high-resolution remote sensing images demonstrate the effectiveness of the proposed HDTFF-Net, and verify that it can greatly improve the classification performance.