Multi-Scale Residual Deep Network for Semantic Segmentation of Buildings with Regularizer of Shape Representation
文献类型：期刊论文
| 作者 | Wang, Chengyi1; Li, Lianfa2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2020-09-01 |
| 卷号 | 12期号:18页码:21 |
| 关键词 | multiple scales residual deep ensemble learning regularizer shape representation semantic segmentation of buildings |
| DOI | 10.3390/rs12182932 |
| 通讯作者 | Li, Lianfa(lilf@lreis.ac.cn) |
| 英文摘要 | It is challenging for semantic segmentation of buildings based on high-resolution remote sensing images, given high variability of appearance and complicated backgrounds of the buildings and their images. In this communication, we proposed an ensemble multi-scale residual deep learning method with the regularizer of shape representation for semantic segmentation of buildings. Based on the U-Net architecture using residual connections and multi-scale ASPP (atrous spatial pyramid pooling) modules, our method introduced the regularizer of shape representation and ensemble learning of multi-scale models to enhance model training and reduce over-fitting. In our method, the shape representation was coded in an antoencoder that was used to encode and reconstruct the shape characteristics of the buildings. In prediction, we consider multi-scale trained models for different resolution inputs and side effects to obtain an optimal semantic segmentation. With the high-resolution image of the Changshan, an island county in China, we used two-thirds of the study region image to train the model and the remaining one-third for the independent test. We obtained the accuracy of 0.98-0.99, mean intersection over union (MIoU) of 0.91-0.93 and Jaccard coefficient of 0.89-0.92 in validation. In the independent test, our method achieved state-of-the-art performance (MIoU: 0.83; Jaccard index: 0.81). By comparing with the existing representative methods on four different data sets, the proposed method consistently improved the learning process and generalization. The study shows important contributions of ensemble learning of multi-scale residual models and regularizer of shape representation to semantic segmentation of buildings. |
| WOS关键词 | EXTRACTION ; INDEX |
| 资助项目 | National Natural Science Foundation of China[41471376] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA19040501] ; project on the Mechanism of Multi-Scale Urban Gray Scale on Thermal Landscape of Institute of Aerospace Information Innovation, Chinese Academy of Sciences |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000580928700001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Sciences ; project on the Mechanism of Multi-Scale Urban Gray Scale on Thermal Landscape of Institute of Aerospace Information Innovation, Chinese Academy of Sciences |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/157193]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Lianfa |
| 作者单位 | 1.Chinese Acad Sci, Aerosp Informat Res Inst, Natl Engn Res Ctr Geomat, Datun Rd, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Datun Rd, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Chengyi,Li, Lianfa. Multi-Scale Residual Deep Network for Semantic Segmentation of Buildings with Regularizer of Shape Representation[J]. REMOTE SENSING,2020,12(18):21. |
| APA | Wang, Chengyi,&Li, Lianfa.(2020).Multi-Scale Residual Deep Network for Semantic Segmentation of Buildings with Regularizer of Shape Representation.REMOTE SENSING,12(18),21. |
| MLA | Wang, Chengyi,et al."Multi-Scale Residual Deep Network for Semantic Segmentation of Buildings with Regularizer of Shape Representation".REMOTE SENSING 12.18(2020):21. |
入库方式： OAI收割
来源：地理科学与资源研究所
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