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MLAE: A Pretraining Method for Automatic Identification of Urban Public Space

文献类型：期刊论文

作者Cheng, Siyuan1; Chen, Huan1; Yao, Ping1; Song, Liuyi2
刊名IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
出版日期2023
卷号20页码:5
关键词Image reconstruction Task analysis Remote sensing Feature extraction Computational modeling Training Decoding MixLabel antoencoder remote sensing semantic segmentation urban public space
ISSN号1545-598X
DOI10.1109/LGRS.2023.3315687
英文摘要This letter proposes a deep-learning-based remote sensing image segmentation method for estimating the proportion of urban public space, which is an important urban planning problem. Remote sensing images contain diverse landforms and different scales of objects, making image segmentation a challenging task. Most current image segmentation methods use convolutional neural networks, which are deep neural networks that can automatically learn image features and perform classification or regression. However, existing convolutional neural networks are usually pretrained on natural image datasets such as ImageNet, which are very different from remote sensing images, resulting in pretrained models that cannot fully exploit the characteristics of remote sensing images. To address this issue, this letter proposes a MixLabel Autoencoder (MLAE) to further pretrain remote sensing images by image reconstruction. Unlike natural images, remote sensing images are complex and difficult to reconstruct; therefore, we use partial labels to guide the reconstruction process. Our method involves replacing random patches of the input image with corresponding labels and reconstructing the patches using an encoder-decoder architecture. Experimental results show that our method achieves higher segmentation accuracy and better visual effects in downstream tasks. Our method provides valuable guidance for urban planning and construction by identifying the proportion of pixels within each type of area in an image.
资助项目National DefenseScience and Technology Key Laboratory Fund Research Project[6142113210302]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001080604700012
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/21141]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Yao, Ping
作者单位1.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
2.Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Hong Kong, Peoples R China
推荐引用方式
GB/T 7714		 Cheng, Siyuan,Chen, Huan,Yao, Ping,et al. MLAE: A Pretraining Method for Automatic Identification of Urban Public Space[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2023,20:5.
APA Cheng, Siyuan,Chen, Huan,Yao, Ping,&Song, Liuyi.(2023).MLAE: A Pretraining Method for Automatic Identification of Urban Public Space.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,20,5.
MLA Cheng, Siyuan,et al."MLAE: A Pretraining Method for Automatic Identification of Urban Public Space".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 20(2023):5.

入库方式： OAI收割

来源：计算技术研究所

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