Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Semantic labeling in very high resolution images via a self-cascaded convolutional neural network | |
Liu, Yongcheng1,2; Fan, Bin1; Wang, Lingfeng1; Bai, Jun3; Xiang, Shiming1; Pan, Chunhong1 | |
发表期刊 | ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING |
ISSN | 0924-2716 |
2018-11-01 | |
卷号 | 145期号:1页码:78-95 |
摘要 | Semantic labeling for very high resolution (VHR) images in urban areas, is of significant importance in a wide range of remote sensing applications. However, many confusing manmade objects and intricate fine-structured objects make it very difficult to obtain both coherent and accurate labeling results. For this challenging task, we propose a novel deep model with convolutional neural networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet). Specifically, for confusing manmade objects, ScasNet improves the labeling coherence with sequential global-to-local contexts aggregation. Technically, multi-scale contexts are captured on the output of a CNN encoder, and then they are successively aggregated in a self-cascaded manner. Meanwhile, for fine-structured objects, ScasNet boosts the labeling accuracy with a coarse-to-fine refinement strategy. It progressively refines the target objects using the low-level features learned by CNN's shallow layers. In addition, to correct the latent fitting residual caused by multi-feature fusion inside ScasNet, a dedicated residual correction scheme is proposed. It greatly improves the effectiveness of ScasNet. Extensive experimental results on three public datasets, including two challenging benchmarks, show that ScasNet achieves the state-of-the-art performance. (C) 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved. |
关键词 | Semantic labeling Convolutional neural networks (CNNs) Multi-scale contexts End-to-end |
DOI | 10.1016/j.isprsjprs.2017.12.007 |
关键词[WOS] | REMOTE-SENSING IMAGERY ; CLASSIFICATION ; RECOGNITION ; FEATURES |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[91438105] ; National Natural Science Foundation of China[61403376] ; National Natural Science Foundation of China[61403375] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61403375] ; National Natural Science Foundation of China[61403376] ; National Natural Science Foundation of China[91438105] |
WOS研究方向 | Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS类目 | Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000449125400006 |
出版者 | ELSEVIER SCIENCE BV |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/22798 |
专题 | 模式识别国家重点实验室_先进时空数据分析与学习 |
通讯作者 | Fan, Bin |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China 3.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, Beijing, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Liu, Yongcheng,Fan, Bin,Wang, Lingfeng,et al. Semantic labeling in very high resolution images via a self-cascaded convolutional neural network[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2018,145(1):78-95. |
APA | Liu, Yongcheng,Fan, Bin,Wang, Lingfeng,Bai, Jun,Xiang, Shiming,&Pan, Chunhong.(2018).Semantic labeling in very high resolution images via a self-cascaded convolutional neural network.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,145(1),78-95. |
MLA | Liu, Yongcheng,et al."Semantic labeling in very high resolution images via a self-cascaded convolutional neural network".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 145.1(2018):78-95. |
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