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Open AccessArticle

Region-Wise Deep Feature Representation for Remote Sensing Images

by Peng Li 1,2, Peng Ren 1,2, Xiaoyu Zhang 3,*, Qian Wang 4, Xiaobin Zhu 4,* and Lei Wang 5
1
College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China
2
State Key Laboratory of Mathematical Engineering and Advanced Computing, Wuxi 214125, China
3
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
4
College of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
5
Academy of Broadcasting Science, SARFT, Beijing 100045, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 871; https://doi.org/10.3390/rs10060871
Received: 8 May 2018 / Revised: 31 May 2018 / Accepted: 1 June 2018 / Published: 5 June 2018
Effective feature representations play an important role in remote sensing image analysis tasks. With the rapid progress of deep learning techniques, deep features have been widely applied to remote sensing image understanding in recent years and shown powerful ability in image representation. The existing deep feature extraction approaches are usually carried out on the whole image directly. However, such deep feature representation strategies may not effectively capture the local geometric invariance of target regions in remote sensing images. In this paper, we propose a novel region-wise deep feature extraction framework for remote sensing images. First, regions that may contain the target information are extracted from one whole image. Then, these regions are fed into a pre-trained convolutional neural network (CNN) model to extract regional deep features. Finally, the regional deep features are encoded by an improved Vector of Locally Aggregated Descriptors (VLAD) algorithm to generate the feature representation for the image. We conducted extensive experiments on remote sensing image classification and retrieval tasks based on the proposed region-wise deep feature extraction framework. The comparison results show that the proposed approach is superior to the existing CNN feature extraction methods. View Full-Text
Keywords: convolutional neural networks (CNNs); feature representation; scene classification; image retrieval convolutional neural networks (CNNs); feature representation; scene classification; image retrieval
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MDPI and ACS Style

Li, P.; Ren, P.; Zhang, X.; Wang, Q.; Zhu, X.; Wang, L. Region-Wise Deep Feature Representation for Remote Sensing Images. Remote Sens. 2018, 10, 871.

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