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A Deep-Local-Global Feature Fusion Framework for High Spatial Resolution Imagery Scene Classification
Open AccessArticle

Improving Remote Sensing Scene Classification by Integrating Global-Context and Local-Object Features

1
Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai Institute of Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China
2
National Satellite Meteorological Center, No. 46, Zhongguancun South Street, Haidian District, Beijing 100081, China
3
The 16th Institute, China Aerospace Science and Technology Corporation, 108 West Hangtian Road, Xi’an 710100, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 734; https://doi.org/10.3390/rs10050734
Received: 2 April 2018 / Revised: 26 April 2018 / Accepted: 6 May 2018 / Published: 9 May 2018
(This article belongs to the Special Issue Deep Learning for Remote Sensing)
Recently, many researchers have been dedicated to using convolutional neural networks (CNNs) to extract global-context features (GCFs) for remote-sensing scene classification. Commonly, accurate classification of scenes requires knowledge about both the global context and local objects. However, unlike the natural images in which the objects cover most of the image, objects in remote-sensing images are generally small and decentralized. Thus, it is hard for vanilla CNNs to focus on both global context and small local objects. To address this issue, this paper proposes a novel end-to-end CNN by integrating the GCFs and local-object-level features (LOFs). The proposed network includes two branches, the local object branch (LOB) and global semantic branch (GSB), which are used to generate the LOFs and GCFs, respectively. Then, the concatenation of features extracted from the two branches allows our method to be more discriminative in scene classification. Three challenging benchmark remote-sensing datasets were extensively experimented on; the proposed approach outperformed the existing scene classification methods and achieved state-of-the-art results for all three datasets. View Full-Text
Keywords: remote sensing; scene classification; convolutional neural networks; global-context feature; local-object-level feature remote sensing; scene classification; convolutional neural networks; global-context feature; local-object-level feature
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Zeng, D.; Chen, S.; Chen, B.; Li, S. Improving Remote Sensing Scene Classification by Integrating Global-Context and Local-Object Features. Remote Sens. 2018, 10, 734.

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