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Deep Feature Fusion for High-Resolution Aerial Scene Classification

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Abstract

The rapid development of remote sensing technology let us acquire a large collection of remote sensing scene images with high resolution. Aerial scene classification has become a crucial problem for understanding high-resolution remote sensing imagery. In this letter, we propose a novel framework for aerial scene classification. Unlike some traditional methods in which the features are produced by using handcrafted feature descriptors, our proposed method uses the raw RGB network stream and the saliency coded network stream to extract two different types of informative features. Then, we further propose a deep feature fusion model to fuse these two sets of features for final classification. The comprehensive performance evaluation of our proposed method is tested on two publicly available remote sensing scene classification benchmarks, i.e., the UC-Merced dataset and the AID dataset. Experimental results show that our proposed method achieves satisfactory results and outperforms the state-of-the-art approaches.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Jingchu University of Technology, Jingmen, China

    Heng Wang

  2. Air Force Enginnering University, Xi’an, China

    Yunlong Yu

Authors
  1. Heng Wang
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  2. Yunlong Yu
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Correspondence to Heng Wang.

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Wang, H., Yu, Y. Deep Feature Fusion for High-Resolution Aerial Scene Classification. Neural Process Lett 51, 853–865 (2020). https://doi.org/10.1007/s11063-019-10119-4

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