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SNUNet3+: A Full-Scale Connected Siamese Network and a Dataset for Cultivated Land Change Detection in High-Resolution Remote-Sensing Images

文献类型：期刊论文

作者Miao, Lizhi1,2; Li, Xinting1,2,3; Zhou, Xinxin3; Yao, Ling4; Deng, Yamei3; Hang, Tian3; Zhou, Yuchao3; Yang, Haozhou3
刊名IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
出版日期2024
卷号62页码:18
ISSN号0196-2892
关键词Feature extraction Remote sensing Transformers Convolutional neural networks Deep learning Decoding Task analysis Change detection deep learning (DL) nonagriculturalization of cultivated land remote sensing (RS) UNet3+
DOI10.1109/TGRS.2023.3344284
通讯作者Miao, Lizhi(miaolz@njupt.edu.cn) ; Yao, Ling(yaoling@lreis.ac.cn)
英文摘要The decline of cultivated land significantly threatens the food supply. In recent years, remote sensing (RS) change detection emerged as a valuable tool for monitoring nonagriculturalization. However, existing deep learning (DL) methods for change detection suffer from the problem of inadequate utilization of feature information during image feature extraction, leading to noisy or inaccurate change maps. To address these challenges, we propose a Siamese network based on full-scale connected UNet (SNUNet3+), whose encoder extracts features from the original images at various levels. Then, it merges all fine-grained spatial information and coarse-grained semantic information into the decoder through full-scale skip connections to obtain the feature maps with complete information. The spatial and channel squeeze and excitation (scSE) attention mechanism is embedded within the decoder subnetwork to enhance the discriminative power of the features. In addition, a deep supervision module is introduced to improve the feature learning capability of the hidden layers and the quality of features. Moreover, accurate cultivated land change detection remains challenging because of the lack of fine-grained detection datasets. To enable the proposed method to achieve cultivated land change detection, we produced a cultivated land change detection dataset containing 5170 pairs of 256 x 256 bitemporal images with a spatial resolution of 1 m. The effectiveness and performance of the proposed method are evaluated on three high-resolution RS change detection datasets. Extensive experimental results show that the proposed method outperforms other state-of-the-art methods, achieving the highest F1 -score of 72.90%, 90.36%, and 96.64% on the CLCD dataset, LEVIR-CD dataset, and PX-CLCD dataset, respectively.
WOS关键词FUSION ; COVER
资助项目State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (a study on the dynamic monitoring of nonagriculturalization of cultivated land based on im
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001136708200016
资助机构State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (a study on the dynamic monitoring of nonagriculturalization of cultivated land based on im
源URL[http://ir.igsnrr.ac.cn/handle/311030/202388]  
专题中国科学院地理科学与资源研究所
通讯作者Miao, Lizhi; Yao, Ling
作者单位1.Nanjing Univ Posts & Telecommun, Sch Geog & Biol Informat, Nanjing 210023, Peoples R China
2.Nanjing Univ Posts & Telecommun, Smart Hlth Big Data Anal & Locat Serv Engn Res Ctr, Nanjing 210023, Peoples R China
3.Nanjing Univ Posts & Telecommun, Sch Geog & Biol Informat, Nanjing 210023, Jiangsu, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Miao, Lizhi,Li, Xinting,Zhou, Xinxin,et al. SNUNet3+: A Full-Scale Connected Siamese Network and a Dataset for Cultivated Land Change Detection in High-Resolution Remote-Sensing Images[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:18.
APA Miao, Lizhi.,Li, Xinting.,Zhou, Xinxin.,Yao, Ling.,Deng, Yamei.,...&Yang, Haozhou.(2024).SNUNet3+: A Full-Scale Connected Siamese Network and a Dataset for Cultivated Land Change Detection in High-Resolution Remote-Sensing Images.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,18.
MLA Miao, Lizhi,et al."SNUNet3+: A Full-Scale Connected Siamese Network and a Dataset for Cultivated Land Change Detection in High-Resolution Remote-Sensing Images".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):18.

入库方式： OAI收割

来源：地理科学与资源研究所

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