CASIA OpenIR  > 空天信息研究中心
Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery
Wang, Guoli1,2; Fan, Bin1; Xiang, Shiming1; Pan, Chunhong1
Source PublicationIEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
2017-09-01
Volume10Issue:9Pages:4104-4115
SubtypeArticle
AbstractScene 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.
KeywordConvolutional Neural Networks (Cnns) Mixed-resolution Representation Remote Sensing Scene Classification Vector Of Locally Aggregated Descriptors (Vlad)
WOS HeadingsScience & Technology ; Technology ; Physical Sciences
DOI10.1109/JSTARS.2017.2705419
WOS KeywordMULTISCALE ; SCALE
Indexed BySCI
Language英语
Funding OrganizationNational 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 Research AreaEngineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEngineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000412626400025
Citation statistics
Cited Times:10[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14526
Collection空天信息研究中心
Affiliation1.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
Recommended Citation
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
Aggregating Rich Hie(878KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Guoli]'s Articles
[Fan, Bin]'s Articles
[Xiang, Shiming]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Guoli]'s Articles
[Fan, Bin]'s Articles
[Xiang, Shiming]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Guoli]'s Articles
[Fan, Bin]'s Articles
[Xiang, Shiming]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Aggregating Rich Hierarchical Features for Scene Classification in Remote Sensing Imagery.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.