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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2020

27.09.2019

Deep Feature Fusion for High-Resolution Aerial Scene Classification

verfasst von: Heng Wang, Yunlong Yu

Erschienen in: Neural Processing Letters | Ausgabe 1/2020

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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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Metadaten
Titel
Deep Feature Fusion for High-Resolution Aerial Scene Classification
verfasst von
Heng Wang
Yunlong Yu
Publikationsdatum
27.09.2019
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-10119-4

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