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Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images
Wang, Hongzhen1,2; Wang, Ying1; Zhang, Qian3; Xiang, Shiming1; Pan, Chunhong1
Source PublicationREMOTE SENSING
2017-05-01
Volume9Issue:5Pages:446
SubtypeArticle
AbstractSemantic segmentation is a fundamental task in remote sensing image processing. The large appearance variations of ground objects make this task quite challenging. Recently, deep convolutional neural networks (DCNNs) have shown outstanding performance in this task. A common strategy of these methods (e.g., SegNet) for performance improvement is to combine the feature maps learned at different DCNN layers. However, such a combination is usually implemented via feature map summation or concatenation, indicating that the features are considered indiscriminately. In fact, features at different positions contribute differently to the final performance. It is advantageous to automatically select adaptive features when merging different-layer feature maps. To achieve this goal, we propose a gated convolutional neural network to fulfill this task. Specifically, we explore the relationship between the information entropy of the feature maps and the label-error map, and then a gate mechanism is embedded to integrate the feature maps more effectively. The gate is implemented by the entropy maps, which are generated to assign adaptive weights to different feature maps as their relative importance. Generally, the entropy maps, i.e., the gates, guide the network to focus on the highly-uncertain pixels, where detailed information from lower layers is required to improve the separability of these pixels. The selected features are finally combined to feed into the classifier layer, which predicts the semantic label of each pixel. The proposed method achieves competitive segmentation accuracy on the public ISPRS 2D Semantic Labeling benchmark, which is challenging for segmentation by only using the RGB images.
KeywordSemantic Segmentation Cnn Deep Learning Isprs Remote Sensing Gate
WOS HeadingsScience & Technology ; Technology
DOI10.3390/rs9050446
WOS KeywordCLASSIFICATION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(91646207 ; Beijing Natural Science Foundation(4162064) ; 91338202 ; 91438105)
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000402573700049
Citation statistics
Cited Times:20[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15117
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
3.Alibaba Grp, Beijing 100102, Peoples R China
Recommended Citation
GB/T 7714
Wang, Hongzhen,Wang, Ying,Zhang, Qian,et al. Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images[J]. REMOTE SENSING,2017,9(5):446.
APA Wang, Hongzhen,Wang, Ying,Zhang, Qian,Xiang, Shiming,&Pan, Chunhong.(2017).Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images.REMOTE SENSING,9(5),446.
MLA Wang, Hongzhen,et al."Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images".REMOTE SENSING 9.5(2017):446.
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