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

Souleyman Chaib; Hongxun Yao; Yanfeng Gu; Moussa Amrani

doi:10.1117/12.2281755

21 July 2017 Deep feature extraction and combination for remote sensing image classification based on pre-trained CNN models

Souleyman Chaib, Hongxun Yao, Yanfeng Gu, Moussa Amrani

Author Affiliations +

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104203D (2017) https://doi.org/10.1117/12.2281755
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

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.

Citation Download Citation

Souleyman Chaib, Hongxun Yao, Yanfeng Gu, and Moussa Amrani "Deep feature extraction and combination for remote sensing image classification based on pre-trained CNN models", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104203D (21 July 2017); https://doi.org/10.1117/12.2281755

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