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Proceedings Paper

Deep feature extraction and combination for remote sensing image classification based on pre-trained CNN models
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Paper Abstract

Understanding a scene provided by Very High Resolution (VHR) satellite imagery has become a more and more challenging problem. In this paper, we propose a new method for scene classification based on different pre-trained Deep Features Learning Models (DFLMs). DFLMs are applied simultaneously to extract deep features from the VHR image scene, and then different basic operators are applied for features combination extracted with different pre-trained Convolutional Neural Networks (CNN) models. We conduct experiments on the public UC Merced benchmark dataset, which contains 21 different areal categories with sub-meter resolution. Experimental results demonstrate the effectiveness of the proposed method, as compared to several state-of-the-art methods.

Paper Details

Date Published: 21 July 2017
PDF: 5 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104203D (21 July 2017); doi: 10.1117/12.2281755
Show Author Affiliations
Souleyman Chaib, Harbin Institute of Technology (China)
Hongxun Yao, Harbin Institute of Technology (China)
Yanfeng Gu, Harbin Institute of Technology (China)
Moussa Amrani, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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