Context-Aware Cascade Network for Semantic Labeling in VHR Image
文献类型：会议论文
| 作者 | Yongcheng Liu1,2 ; Bin Fan1; Jun Bai3; Lingfeng Wang1; Shiming Xiang1; Chunhong Pan1
|
| 出版日期 | 2017 |
| 会议日期 | 2017-9-17 |
| 会议地点 | Beijing, CHINA |
| 英文摘要 | Semantic labeling for the very high resolution (VHR) image of urban areas is challenging, because of many complex manmade objects with different materials and fine-structured objects located together. Under the framework of convolutional neural networks (CNNs), this paper proposes a novel end-toend network for semantic labeling. Specifically, our network not only improves the labeling accuracy of complex manmade objects by aggregating multiple context semantics with a cascaded architecture, but also refines fine-structured objects by utilizing the low-level detail in shallow layers of CNNs with a hierarchical pyramid structure. Throughout the network, a dedicated residual correction scheme is employed to amend the latent fitting residual. As a result of these specific components, the whole model works in a global-to-local and coarseto-fine manner. Experimental results show that our network outperforms the state-of-the-art methods on the large-scale ISPRS Vaihingen 2D Semantic Labeling Challenge dataset. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/20352]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
| 作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Computer and Control Engineering, University of Chinese Academy of Sciences 3.Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Yongcheng Liu,Bin Fan,Jun Bai,et al. Context-Aware Cascade Network for Semantic Labeling in VHR Image[C]. 见:. Beijing, CHINA. 2017-9-17. |
入库方式： OAI收割
来源：自动化研究所
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