Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine
文献类型：期刊论文
| 作者 | Wang, Jun1; Song, Jingwei1; Chen, Mingquan1; Yang, Zhi1 |
| 刊名 | INTERNATIONAL JOURNAL OF REMOTE SENSING
 |
| 出版日期 | 2015 |
| 卷号 | 36期号:12页码:404-412 |
| 通讯作者 | Wang, J (reprint author), Univ Chinese Acad Sci, Inst Remote Sensing & Digital Earth Chinese Acad, Beijing, Peoples R China. |
| 英文摘要 | Extracting road networks from very-high-resolution (VHR) aerial and satellite imagery has been a long-standing problem. In this article, a neural-dynamic tracking framework is proposed to extract road networks based on deep convolutional neural networks (DNN) and a finite state machine (FSM). Inspired by autonomous mobile systems, the authors train a DNN to recognize the pattern of input data, which is an image patch extracted in a detection window centred at the current location of the tracker. The pattern is predefined according to the environment and associated with the states in the FSM. A vector-guided sampling method is proposed to generate the training data set for the DNN, which extracts massive image-direction pairs from the imagery and existing vector road maps. In the tracking procedure, the size of the detection window is determined by a fusion strategy and the extracted image patches represent the orientation features of the road (local environment) that can be recognized by the trained DNN. The reactive unit in FSM associates states with behaviours of the tracker while continually modifying the orientation to follow the road and generating a sequence of states and locations. In this way, our framework combines the DNN and FSM. DNN acts as a key component to recognize patterns from a complex and changing environment; FSM translates the recognized patterns to states and controls the behaviour of the tracker. The results illustrate that our approach is more accurate and efficient than the traditional ones. |
| 研究领域[WOS] | Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000357295800007 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38510]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1.[Wang, Jun 2.Song, Jingwei 3.Yang, Zhi] Univ Chinese Acad Sci, Inst Remote Sensing & Digital Earth Chinese Acad, Beijing, Peoples R China 4.[Chen, Mingquan] Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA |
| 推荐引用方式 GB/T 7714 | Wang, Jun,Song, Jingwei,Chen, Mingquan,et al. Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2015,36(12):404-412. |
| APA | Wang, Jun,Song, Jingwei,Chen, Mingquan,&Yang, Zhi.(2015).Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine.INTERNATIONAL JOURNAL OF REMOTE SENSING,36(12),404-412. |
| MLA | Wang, Jun,et al."Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine".INTERNATIONAL JOURNAL OF REMOTE SENSING 36.12(2015):404-412. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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