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Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery
Wang, Guoli1,2; Fan, Bin1; Xiang, Shiming1; Pan, Chunhong1
2017-09-01
发表期刊IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷号10期号:9页码:4104-4115
文章类型Article
摘要Scene classification is one of the most important issues in remote sensing image processing. To obtain a high discriminative feature representation for an image to be classified, traditional methods usually consider to densely accumulate hand-crafted low-level descriptors (e.g., scale-invariant feature transform) by feature encoding techniques. However, the performance is largely limited by the hand-crafted descriptors as they are not capable of describing the rich semantic information contained in various remote sensing images. To alleviate this problem, we propose a novel method to extract discriminative image features from the rich hierarchical information contained in convolutional neural networks (CNNs). Specifically, the low-level and middle-level intermediate convolutional features are, respectively, encoded by vector of locally aggregated descriptors (VLAD) and then reduced by principal component analysis to obtain hierarchical global features; meanwhile, the fully connected features are average pooled and subsequently normalized to form new global features. The proposed encoded mixed-resolution representation (EMR) is the concatenation of all the above-mentioned global features. Due to the usage of encoding strategies (VLAD and average pooling), our method can deal with images of different sizes. In addition, to reduce the computational consumption in the training stage, we directly extract EMR from VGG-VD and ResNet pretrained on the ImageNet dataset. We show in this paper that CNNs pretrained on the natural image dataset are more easily applied to the remote sensing dataset when the local structure similarity between two datasets is higher. Experimental evaluations on the UC-Merced and Brazilian Coffee Scenes datasets demonstrate that our method is superior to the state of the art.
关键词Convolutional Neural Networks (Cnns) Mixed-resolution Representation Remote Sensing Scene Classification Vector Of Locally Aggregated Descriptors (Vlad)
WOS标题词Science & Technology ; Technology ; Physical Sciences
DOI10.1109/JSTARS.2017.2705419
关键词[WOS]MULTISCALE ; SCALE
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61403375 ; Priority Academic Program Development of Jiangsu Higher Education Institutions ; Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology ; 61472119 ; 61573352 ; 61375024 ; 91338202 ; 91646207)
WOS研究方向Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:000412626400025
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/14526
专题空天信息研究中心
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100190, Peoples R China
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GB/T 7714
Wang, Guoli,Fan, Bin,Xiang, Shiming,et al. Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2017,10(9):4104-4115.
APA Wang, Guoli,Fan, Bin,Xiang, Shiming,&Pan, Chunhong.(2017).Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,10(9),4104-4115.
MLA Wang, Guoli,et al."Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 10.9(2017):4104-4115.
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