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Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection
Zhang, Yuanlin1,2; Yuan, Yuan3; Feng, Yachuang1; Lu, Xiaoqiang1
Department光学影像学习与分析中心
2019-08
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892;1558-0644
Volume57Issue:8Pages:5535-5548
Contribution Rank1
Abstract

Object detection is a basic issue of very high-resolution remote sensing images (RSIs) for automatically labeling objects. At present, deep learning has gradually gained the competitive advantage for remote sensing object detection, especially based on convolutional neural networks (CNNs). Most of the existing methods use the global information in the fully connected feature vector and ignore the local information in the convolutional feature cubes. However, the local information can provide spatial information, which is helpful for accurate localization. In addition, there are variable factors, such as rotation and scaling, which affect the object detection accuracy in RSIs. In order to solve these problems, this paper presents a hierarchical robust CNN. First, multiscale convolutional features are extracted to represent the hierarchical spatial semantic information. Second, multiple fully connected layer features are stacked together so as to improve the rotation and scaling robustness. Experiments on two data sets have shown the effectiveness of our method. In addition, a largescale high-resolution remote sensing object detection data set is established to make up for the current situation that the existing data set is insufficient or too small.

KeywordConvolutional neural networks (CNNs) hierarchical robust CNN (HRCNN) hierarchical spatial semantic (HSS) object detection remote sensing images (RSIs) rotation and scaling robust enhancement (RSRE)
DOI10.1109/TGRS.2019.2900302
Indexed BySCI ; EI
Language英语
WOS IDWOS:000476805800025
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI Accession Number20193107243616
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31611
Collection光学影像学习与分析中心
Corresponding AuthorLu, Xiaoqiang
Affiliation1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol, Xian 710119, Shaanxi, Peoples R China
2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China
3.Northwestern Polytech Univ, Ctr Opt Imagery Anal & Learning, Sch Comp Sci, Xian 710072, Shaanxi, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Yuanlin,Yuan, Yuan,Feng, Yachuang,et al. Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2019,57(8):5535-5548.
APA Zhang, Yuanlin,Yuan, Yuan,Feng, Yachuang,&Lu, Xiaoqiang.(2019).Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,57(8),5535-5548.
MLA Zhang, Yuanlin,et al."Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 57.8(2019):5535-5548.
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