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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
Source PublicationISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN0924-2716
2018-11-01
Volume145Issue:1Pages:78-95
Abstract

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.

KeywordSemantic labeling Convolutional neural networks (CNNs) Multi-scale contexts End-to-end
DOI10.1016/j.isprsjprs.2017.12.007
WOS KeywordREMOTE-SENSING IMAGERY ; CLASSIFICATION ; RECOGNITION ; FEATURES
Indexed BySCI
Language英语
Funding ProjectNational 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 Research AreaPhysical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000449125400006
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:16[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22798
Collection先进数据分析与学习团队
Corresponding AuthorFan, Bin
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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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