A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China
文献类型：期刊论文
| 作者 | Hu, Yunfeng1,2; Zhang, Qianli1,2; Zhang, Yunzhi1,2; Yan, Huimin1,2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2018-12-01 |
| 卷号 | 10期号:12页码:16 |
| 关键词 | land cover/land use image classification automatic mapping accuracy evaluation methods comparison Landsat OLI imagery |
| ISSN号 | 2072-4292 |
| DOI | 10.3390/rs10122053 |
| 通讯作者 | Hu, Yunfeng(huyf@lreis.ac.cn) ; Zhang, Qianli(zhangql@lreis.ac.cn) |
| 英文摘要 | Land cover and its dynamic information is the basis for characterizing surface conditions, supporting land resource management and optimization, and assessing the impacts of climate change and human activities. In land cover information extraction, the traditional convolutional neural network (CNN) method has several problems, such as the inability to be applied to multispectral and hyperspectral satellite imagery, the weak generalization ability of the model and the difficulty of automating the construction of a training database. To solve these problems, this study proposes a new type of deep convolutional neural network based on Landsat-8 Operational Land Imager (OLI) imagery. The network integrates cascaded cross-channel parametric pooling and average pooling layer, applies a hierarchical sampling strategy to realize the automatic construction of the training dataset, determines the technical scheme of model-related parameters, and finally performs the automatic classification of remote sensing images. This study used the new type of deep convolutional neural network to extract land cover information from Qinhuangdao City, Hebei Province, and compared the experimental results with those obtained by traditional methods. The results show that: (1) The proposed deep convolutional neural network (DCNN) model can automatically construct the training dataset and classify images. This model performs the classification of multispectral and hyperspectral satellite images using deep neural networks, which improves the generalization ability of the model and simplifies the application of the model. (2) The proposed DCNN model provides the best classification results in the Qinhuangdao area. The overall accuracy of the land cover data obtained is 82.0%, and the kappa coefficient is 0.76. The overall accuracy is improved by 5% and 14% compared to the support vector machine method and the maximum likelihood classification method, respectively. |
| WOS关键词 | CLASSIFICATION ; INTEGRATION ; ALGORITHMS ; MACHINE ; SAR |
| 资助项目 | National Key Research and Development Program of China[2016YFC0503702] ; Strategic Priority Research Program of CAS[XDA19040301] ; Key Project of High-Resolution Earth Observation of China[00-Y30B14-9001-14/16] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000455637600195 |
| 出版者 | MDPI |
| 资助机构 | National Key Research and Development Program of China ; Strategic Priority Research Program of CAS ; Key Project of High-Resolution Earth Observation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/50475]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Hu, Yunfeng; Zhang, Qianli |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.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 | Hu, Yunfeng,Zhang, Qianli,Zhang, Yunzhi,et al. A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China[J]. REMOTE SENSING,2018,10(12):16. |
| APA | Hu, Yunfeng,Zhang, Qianli,Zhang, Yunzhi,&Yan, Huimin.(2018).A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China.REMOTE SENSING,10(12),16. |
| MLA | Hu, Yunfeng,et al."A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China".REMOTE SENSING 10.12(2018):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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