A remote sensing image scene classification framework based Efficientnet-b7

Yuanhui Chen; Kaile Ye; Chunmei Xu; Lili Zhan

doi:10.1117/12.2625586

9 December 2021 A remote sensing image scene classification framework based Efficientnet-b7

Yuanhui Chen, Kaile Ye, Chunmei Xu, Lili Zhan

Proceedings Volume 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021); 1212903 (2021) https://doi.org/10.1117/12.2625586
Event: 2021 International Conference on Environmental Remote Sensing and Big Data, 2021, Wuhan, China

Abstract

Remote sensing image scene classification (RSSC) is a hot topic in remote sensing. Aiming at the problem of remote sensing image of limited labeling samples and class-imbalanced, we proposed a RSSC framework based on efficientnetB7. The framework uses mirroring, rotation, cropping, HSV disturbance, and gamma transform to improve the problem of class-imbalance, and restricts the rotation angle of the high-rise-sparse-buildings to make it in line with the actual situation., and then the pre-trained model is used for training. The results show that the kappa and OA of the model increased 0.139 and 0.055, respectively, and the classification deviation caused by class-imbalance is alleviated.

Citation Download Citation

Yuanhui Chen, Kaile Ye, Chunmei Xu, and Lili Zhan "A remote sensing image scene classification framework based Efficientnet-b7", Proc. SPIE 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021), 1212903 (9 December 2021); https://doi.org/10.1117/12.2625586

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KEYWORDS

Data modeling

Remote sensing

Scene classification

Classification systems

Image classification

Statistical modeling

Neural networks

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