@inproceedings{53015490a4764a34af9616465df15009,
title = "Deep feature extraction and combination for remote sensing image classification based on pre-trained CNN models",
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.",
keywords = "deep feature learning, feature combination, pre-trained CNN, scene classification",
author = "Souleyman Chaib and Hongxun Yao and Yanfeng Gu and Moussa Amrani",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; 9th International Conference on Digital Image Processing, ICDIP 2017 ; Conference date: 19-05-2017 Through 22-05-2017",
year = "2017",
doi = "10.1117/12.2281755",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Xudong Jiang and Falco, \{Charles M.\}",
booktitle = "Ninth International Conference on Digital Image Processing, ICDIP 2017",
address = "美国",
}