Gated Convolutional Neural Network for Semantic Segmentation in High-Resolution Images
Wang, Hongzhen1,2; Wang, Ying1; Zhang, Qian3; Xiang, Shiming1; Pan, Chunhong1
2017-05-01
发表期刊REMOTE SENSING
卷号9期号:5页码:446
文章类型Article
摘要Semantic 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.
关键词Semantic Segmentation Cnn Deep Learning Isprs Remote Sensing Gate
WOS标题词Science & Technology ; Technology
DOI10.3390/rs9050446
关键词[WOS]CLASSIFICATION
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(91646207 ; Beijing Natural Science Foundation(4162064) ; 91338202 ; 91438105)
WOS研究方向Remote Sensing
WOS类目Remote Sensing
WOS记录号WOS:000402573700049
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15117
专题模式识别国家重点实验室_先进数据分析与学习
作者单位1.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
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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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