| 基于深度对抗域适应的高分辨率遥感影像跨域分类 |
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| 引用本文： | 滕文秀,王妮,陈泰生,王本林,陈梦琳,施慧慧.基于深度对抗域适应的高分辨率遥感影像跨域分类[J].激光与光电子学进展,2019,56(11):228-238. |
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| 作者姓名： | 滕文秀 王妮 陈泰生 王本林 陈梦琳 施慧慧 |
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| 作者单位： | 南京林业大学林学院,江苏南京,210037;滁州学院地理信息与旅游学院,安徽滁州239000;安徽省地理信息智能感知与服务工程实验室,安徽滁州239000;滁州学院地理信息与旅游学院,安徽滁州239000;安徽省地理信息智能感知与服务工程实验室,安徽滁州239000;河海大学地球科学与工程学院,江苏南京210098;安徽省地理信息智能感知与服务工程实验室,安徽滁州,239000 |
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| 摘 要： | 提出一种基于深度对抗域适应的高分辨率遥感影像跨域分类方法。利用深度卷积神经网络VGG16(Visual Geometry Group)学习场景影像的深度特征,然后利用对抗学习方法最小化源域和目标域特征分布差异。利用RSI-CB256(Remote Sensing Image Classification Benchmark)、NWPU-RESISC45(Northwestern Polytechnical University Remote Sensing Image Scene Classification)和AID(Aerial Image data set)数据集构建源域数据集,并将UC-Merced(University of California,Merced)和WHU-RS 19(Wuhan University Remote Sensing)两个数据集作为目标域数据集进行实验,实验结果表明,所提方法在目标域数据集没有标签的情况下,能够提高模型对目标域数据集的泛化能力。
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| 关 键 词： | 遥感 场景分类 无监督域适应 卷积神经网络 生成对抗网络 |
Deep Adversarial Domain Adaptation Method for Cross-Domain Classification in High-Resolution Remote Sensing Images |
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| Teng Wenxiu,Wang Ni,Chen Taisheng,Wang Benlin,Chen Menglin,Shi Huihui.Deep Adversarial Domain Adaptation Method for Cross-Domain Classification in High-Resolution Remote Sensing Images[J].Laser & Optoelectronics Progress,2019,56(11):228-238. |
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| Authors: | Teng Wenxiu Wang Ni Chen Taisheng Wang Benlin Chen Menglin Shi Huihui |
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| Affiliation: | (College of Forest,Nanjing Forestry University,Nanjing,Jiangsu 210037,China;School of Geographic Information and Tourism,Chuzhou University,Chuzhou,Anhui 239000,China;Anhui Engineering Laboratory of Geographical Information Intelligent Sensor and Service,Chuzhou,Anhui 239000,China;School of Earth Sciences and Engineering,Hohai University,Nanjing,Jiangsu 210098,China) |
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| Abstract: | Teng Wenxiu;Wang Ni;Chen Taisheng;Wang Benlin;Chen Menglin;Shi Huihui(College of Forest,Nanjing Forestry University,Nanjing,Jiangsu 210037,China;School of Geographic Information and Tourism,Chuzhou University,Chuzhou,Anhui 239000,China;Anhui Engineering Laboratory of Geographical Information Intelligent Sensor and Service,Chuzhou,Anhui 239000,China;School of Earth Sciences and Engineering,Hohai University,Nanjing,Jiangsu 210098,China) |
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| Keywords: | remote sensing scene classification unsupervised domain adaptation convolutional neural network generative adversarial networks |
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