Attention-Aware Deep Feature Embedding for Remote Sensing Image Scene Classification

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 16; pp. 1 - 14
• Main Authors: Chen, Xiaoning, Han, Zonghao, Li, Yong, Ma, Mingyang, Mei, Shaohui, Cheng, Wei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Attention Mechanism; Benchmarks; Classification; Convolutional Neural Network (CNN); Dual Attention-Aware(DAA); Embedding; Feature extraction; Feature maps; Image classification; Information processing; Methods; Modules; Neural networks; Remote sensing; Remote Sensing (RS); Scene Classification; Semantics
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2022.3229729

Due to the wide application of Remote Sensing (RS) image scene classification, more and more scholars activate great attention to it. With the development of the Convolutional Neural Network (CNN), the CNN-based methods of RS image scene classification have made impressive progress. In the existing works, most of the architectures just considered the global information of the RS images. However, the global information contains a large number of redundant areas that diminish the classification performance and ignore the local information that reflects more fine spatial details of local objects. Furthermore, most CNN-based methods assign the same weights to each feature vector causing the mode to fail to discriminate the crucial features. In this paper, a novel method by Two-branch Deep Feature Embedding with a Dual Attention-Aware module for RS image scene classification is proposed. In order to mine more complementary information, we extract global semantic-based features of high level and local object-based features of low level by the Two-branch Deep Feature Embedding (TDFE) module. Then, to focus selectively on the key global-semantics feature maps as well as the key local regions, we propose a Dual Attention-Aware (DAA) module to attain those key information. We conduct extensive experiments to verify the superiority of our proposed method, and the experimental results obtained on two widely used RS scene classification benchmarks demonstrate the effectiveness of the proposed method.