Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

作     者：Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asoke K.Nandi 

作者机构：School of Computer Science and TechnologyXi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an Key Laboratory of Big Data and Intelligent ComputingXi'anChina School of Electronic Information and Artificial IntelligenceShaanxi University of Science and TechnologyXi'anChina School of ComputerCentral China Normal UniversityWuhanChina Department of Electronic and Electrical EngineeringBrunel University LondonLondonUK Xi'an Jiaotong UniversityXi'anChina 

出 版 物：《CAAI Transactions on Intelligence Technology》 (智能技术学报（英文）)

年 卷 期：2023年第8卷第4期

页      面：1178-1190页

核心收录：

学科分类：0710[理学-生物学] 04[教育学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported in part by the National Natural Science Foundation of China under Grant 62201452,2271296 and 62201453 in part by the Natural Science Basic Research Programme of Shaanxi under Grant 2022JQ‐592 in part by the Special Construction Fund for Key Disciplines of Shaanxi Provincial Higher Education,in part by the Natural Science Basic Research Program of Shaanxi under Grant 2021JC‐47 in part by Scientific Research Program Funded by Shaanxi Provincial Education Department under Grant 22JK0568. 

主　　题：deep learning image analysis image classification information fusion 

摘      要：Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential applications.Most of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ASR.However,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene images.In addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local features.To tackle these problems,this study proposes a scale‐wise interaction fusion and knowledge distillation(SIF‐KD)network for learning robust and discriminative features with scaleinvariance and background‐independent information.The main highlights of this study include two aspects.On the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene images.Specifically,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local features.On the other hand,a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during training.Comprehensive experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.