DAFT: Differential Feature Extraction Network Based on Adaptive Frequency Transformer for Remote Sensing Change Detection

被引:6
作者
Fu, Zhaojin [1 ]
Li, Jinjiang [2 ]
Chen, Zheng [2 ]
Ren, Lu [2 ]
Hua, Zhen [1 ]
机构
[1] Shandong Technol & Business Univ, Sch Informat & Elect Engn, Yantai 264005, Peoples R China
[2] Shandong Technol & Business Univ, Sch Comp Sci & Technol, Yantai 264005, Peoples R China
基金
中国国家自然科学基金;
关键词
Feature extraction; Remote sensing; Transformers; Image reconstruction; Computer architecture; Task analysis; Semantics; Attention mechanism; change detection (CD); remote sensing; transformer; IMAGES;
D O I
10.1109/JSTARS.2023.3280589
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Remote sensing change detection is an important research direction in the field of remote sensing. It is mainly used to focus on the changing information on the ground over a period of time, and to identify the interested change targets from it. The rapid changes in ground information due to social development undoubtedly increase the importance of change detection. Currently, change detection methods still have some shortcomings in dealing with complex targets, environmental noise, and other aspects. Therefore, we propose a differential feature extraction network based on adaptive frequency transformer for remote sensing change detection (DAFT). Adaptive frequency transformer (AFFormer) is capable of separating change targets and environments from a frequency perspective and capturing long-range dependencies between feature information through self-attention. Therefore, in DAFT, we use AFFormer as the backbone network to extract feature information from bitemporal images, enhancing our focus on change targets while obtaining richer and more detailed information. To our knowledge, this is the first time that AFFormer has been applied in the field of CD. To address the issues of missing location information of change targets and insufficient local feature correlation, DAFT proposes a differential features enhancement module in the feature reconstruction stage of change targets. In addition, DAFT uses DO-Conv to enhance pixel correlation calculation in convolutional operations, allowing the network to focus on richer information. By outputting results at different scales during the feature reconstruction stage, DAFT computes multiple losses that are summed up to guide the training process for better performance. The experimental results prove that DAFT achieves high versus mainstream networks. On LEVIR-CD the F1 is 91.814 and the IoU is 84.866; on WHU-CD the F1 is 92.085 and the IoU is 85.330; on GZ-CD the F1 is 86.065 and the IoU is 74.512.
引用
收藏
页码:5061 / 5076
页数:16
相关论文
共 63 条
[1]   A TRANSFORMER-BASED SIAMESE NETWORK FOR CHANGE DETECTION [J].
Bandara, Wele Gedara Chaminda ;
Patel, Vishal M. .
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, :207-210
[2]   An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images [J].
Bazi, Y ;
Bruzzone, L ;
Melgani, F .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005, 43 (04) :874-887
[3]   DO-Conv: Depthwise Over-Parameterized Convolutional Layer [J].
Cao, Jinming ;
Li, Yangyan ;
Sun, Mingchao ;
Chen, Ying ;
Lischinski, Dani ;
Cohen-Or, Daniel ;
Chen, Baoquan ;
Tu, Changhe .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 31 :3726-3736
[4]   Remote Sensing Image Change Detection With Transformers [J].
Chen, Hao ;
Qi, Zipeng ;
Shi, Zhenwei .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
[5]   A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection [J].
Chen, Hao ;
Shi, Zhenwei .
REMOTE SENSING, 2020, 12 (10)
[6]   DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images [J].
Chen, Jie ;
Yuan, Ziyang ;
Peng, Jian ;
Chen, Li ;
Huang, Haozhe ;
Zhu, Jiawei ;
Liu, Yu ;
Li, Haifeng .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 :1194-1206
[7]   DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [J].
Chen, Liang-Chieh ;
Papandreou, George ;
Kokkinos, Iasonas ;
Murphy, Kevin ;
Yuille, Alan L. .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (04) :834-848
[8]   A Siamese Network Based U-Net for Change Detection in High Resolution Remote Sensing Images [J].
Chen, Tao ;
Lu, Zhiyuan ;
Yang, Yue ;
Zhang, Yuxiang ;
Du, Bo ;
Plaza, Antonio .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2022, 15 :2357-2369
[9]   ISNet: Towards Improving Separability for Remote Sensing Image Change Detection [J].
Cheng, Gong ;
Wang, Guangxing ;
Han, Junwei .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
[10]  
Daudt RC, 2018, IEEE IMAGE PROC, P4063, DOI 10.1109/ICIP.2018.8451652