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	Pairwise Comparison Network for Remote Sensing Scene Classification
	Zhang, Yue1,2; Zheng, Xiangtao2; Lu, Xiaoqiang2
英文摘要	Remote sensing scene classification aims to assign a specific semantic label to a remote sensing image. Recently, convolutional neural networks have greatly improved the performance of remote sensing scene classification. However, some confused images may be easily recognized as the incorrect category, which generally degrade the performance. The differences between image pairs can be used to distinguish image categories. This paper proposed a pairwise comparison network, which contains two main steps: pairwise selection and pairwise representation. The proposed network first selects similar image pairs, and then represents the image pairs with pairwise representations. The self-representation is introduced to highlight the informative parts of each image itself, while the mutual-representation is proposed to capture the subtle differences between image pairs. Comprehensive experimental results on two challenging datasets (AID, NWPU-RESISC45) demonstrate the effectiveness of the proposed network. The code are provided in https://github.com/spectralpublic/PCNet.git. Copyright © 2022, The Authors. All rights reserved.
	2022-05-17
产权排序	1
语种	英语
内容类型	预印本
源URL	[http://ir.opt.ac.cn/handle/181661/95983]
专题	西安光学精密机械研究所_光学影像学习与分析中心
作者单位	1.The University of Chinese Academy of Sciences, Beijing; 100049, China 2.The Key Laboratory of Spectral Imaging Technology CAS, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Shaanxi, Xi’an; 710119, China;
推荐引用方式 GB/T 7714	Zhang, Yue,Zheng, Xiangtao,Lu, Xiaoqiang. Pairwise Comparison Network for Remote Sensing Scene Classification. 2022.

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