A novel multi-attention fusion network with dilated convolution and label smoothing for remote sensing image retrieval. Issue 4 (16th February 2022)

• Record Type: Journal Article
• Authors: Wang, Siyuan; Hou, Dongyang; Xing, Huaqiao
• Is Part Of: International journal of remote sensing. Volume 43:Issue 4(2022)
• Page Start: 1306
• Page End: 1322
• Publication Date: 2022-02-16
• Subjects: remote sensing image retrieval -- contextual transformer attention -- convolutional neural network -- dilated convolution -- label smoothing; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2022.2035465
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Convolutional neural networks (CNNs) have proved to achieve state-of-the-art performance in content-based remote sensing image retrieval (CBRSIR). However, CNNs cannot focus on discriminative features of important objects, resulting in unsatisfactory retrieval performance with complex backgrounds and small objects. We therefore propose a multi-attention fusion network with dilated convolution and label smoothing for CBRSIR. First, a dilated convolutional layer is used to replace the fifth convolutional layer in the network to obtain a large receptive field. Then, a contextual transformer attention (CoT) and an efficient channel attention (ECA) are fused together for spatial-wise and channel-wise discriminative features, respectively. The multi-attention module is embedded between the newly added dilated convolutional layer and the followed average pooling layer. Besides, in order to enhance the differences between the discriminative features of those correct and incorrect classes, label smoothing is used to replace the cross-entropy loss function. Some ablation experiments are conducted on six benchmark datasets. Compared to the baseline AlextNet with the above different modules, the mAP values of the proposed network improve by 14.84% to 24.21%. The results indicate that our network can significantly improve the retrieval performance. In addition, we have also conducted some experiments for network migration and comparison with some recent methods (e.g. a triplet deep metric learning and deep feature learning with latent relationship embedding network). Experimental results illustrate that our network can be effectively migrated to other similar CNN models and can achieve state-of-the-art or competitive results.