| 土地利用/覆被深度学习遥感分类研究综述 |
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| 引用本文： | 冯权泷,牛博文,朱德海,陈泊安,张超,杨建宇.土地利用/覆被深度学习遥感分类研究综述[J].农业机械学报,2022,53(3):1-17. |
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| 作者姓名： | 冯权泷 牛博文 朱德海 陈泊安 张超 杨建宇 |
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| 作者单位： | 中国农业大学 |
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| 基金项目： | 国家重点研发计划项目（2021YFE0102300）和国家自然科学基金项目（42001367） |
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| 摘 要： | 基于遥感分类实现高精度的土地利用和土地覆被制图是研究热点问题.近年来,以卷积神经网络为代表的深度学习在计算机视觉领域取得了长足发展,同时也被引入到土地利用/覆被遥感制图领域.相比于经典机器学习,深度学习的优势表现为能够自适应提取与分类任务最相关的特征,其缺陷表现为分类精度的提高依赖于海量标签样本.基于深度学习在土地利用...
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| 关 键 词： | 深度学习 土地利用 土地覆被 遥感分类 样本-模型-算法 |
| 收稿时间： | 2022/1/26 0:00:00 |
Review for Deep Learning in Land Use and Land Cover Remote Sensing Classification |
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| FENG Quanlong,NIU Bowen,ZHU Dehai,CHEN Boan,ZHANG Chao,YANG Jianyu.Review for Deep Learning in Land Use and Land Cover Remote Sensing Classification[J].Transactions of the Chinese Society of Agricultural Machinery,2022,53(3):1-17. |
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| Authors: | FENG Quanlong NIU Bowen ZHU Dehai CHEN Boan ZHANG Chao YANG Jianyu |
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| Institution: | China Agricultural University |
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| Abstract: | Accurate land use and land cover (LULC) mapping based on remote sensing image classification has been a hot topic nowadays. Recently, deep learning, especially convolutional neural network, has achieved promising results in computer vision tasks, which has also been introduced into the field of LULC mapping. Compared with classic machine learning methods, deep learning is capable of extracting the most representative features from remote sensing images, however, its performance is depended on massive labeled data. Considering deep learning has been widely used in LULC classification, the objective was to provide a comprehensive review of deep learning from the following perspectives as sample dataset, model structure and training strategy. Specifically, from the perspective of samples, the most commonly used LULC sample dataset was summarized and their academic influence was analyzed. From the perspective of models, the latest research of deep learning models were reviewed, including convolutional neural network, recurrent neural network, fully convolutional network. From the perspective of training strategies, various training methods that could tackle the data-hunger issue of deep learning were summarized, including active learning, semi-supervised learning, weakly-supervised learning, self-supervised learning, transfer learning. Finally, an outlook of deep learning in LULC mapping was provided, which was still from three perspectives of sample dataset, model structure and training strategy. Through the construction of large-scale LULC sample dataset, improvement of deep learning model structure and the increase of spatial-temporal generalization capability under limited samples, LULC remote sensing classification could yield a better performance and accuracy in future study. |
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| Keywords: | deep learning land use land cover remote sensing classification sample-model-strategy |
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