Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks
文献类型：期刊论文
| 作者 | Peethambaran, Jiju3; Sun, Lan; Chen, Chuqun1; Ke, Yinghai; Chen, Dong2; Zhong, Ruofei; Zhang, Zhenxin; Liu, Yibo |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2018 |
| 卷号 | 11期号:10页码:3688 |
| 关键词 | Building detection convolutional neural networks (CNNs) candidate building regions multilevel framework remote sensing images |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2018.2866284 |
| 英文摘要 | In this paper, we propose a hierarchical building detection framework based on deep learning model, which focuses on accurately detecting buildings from remote sensing images. To this end, we first construct the generation model of the multilevel training samples using the Gaussian pyramid technique to learn the features of building objects at different scales and spatial resolutions. Then, the building region proposal networks are put forward to quickly extract candidate building regions, thereby increasing the efficiency of the building object detection. Based on the candidate building regions, we establish the multilevel building detection model using the convolutional neural networks (CNNs), from which the generic image features of each building region proposal are calculated. Finally, the obtained features are provided as inputs for training CNNs model, and the learned model is further applied to test images for the detection of unknown buildings. Various experiments using the Datasets I and II (in Section V-A) show that the proposed framework increases the mean average precision values of building detection by 3.63%, 3.85%, and 3.77%, compared with the state-of-the-art methods, i.e., Method IV. Besides, the proposed method is robust to the buildings having different spatial textures and types. |
| 源URL | [http://ir.scsio.ac.cn/handle/344004/17565]  |
| 专题 | 南海海洋研究所_热带海洋环境国家重点实验室(LTO) |
| 作者单位 | 1.St Marys Univ, Dept Math & Comp, Halifax, NS B3H 3C3, Canada 2.Capital Normal Univ, Adv Innovat Ctr Imaging Technol, Beijing 100048, Peoples R China 3.Nanjing Forestry Univ, Coll Civil Engn, Nanjing 210037, Jiangsu, Peoples R China 4.Chinese Acad Sci, South China Sea Inst Oceanol, Guangdong Key Lab Ocean Remote Sensing, Guangzhou 510301, Guangdong, Peoples R China |
| 推荐引用方式 GB/T 7714 | Peethambaran, Jiju,Sun, Lan,Chen, Chuqun,et al. Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2018,11(10):3688, 3700. |
| APA | Peethambaran, Jiju.,Sun, Lan.,Chen, Chuqun.,Ke, Yinghai.,Chen, Dong.,...&Liu, Yibo.(2018).Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,11(10),3688. |
| MLA | Peethambaran, Jiju,et al."Multilevel Building Detection Framework in Remote Sensing Images Based on Convolutional Neural Networks".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 11.10(2018):3688. |
入库方式： OAI收割
来源：南海海洋研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。