RAFF: remote sensing airplane fine grained classification with few shot learning

Xu Chen; HongBing Ma; ShengJin Wang

doi:10.1117/12.2669648

24 February 2023 RAFF: remote sensing airplane fine grained classification with few shot learning

Xu Chen, HongBing Ma, ShengJin Wang

Proceedings Volume 12590, Third International Conference on Computer Vision and Information Technology (CVIT 2022); 125900A (2023) https://doi.org/10.1117/12.2669648
Event: 2022 3rd International Conference on Computer Vision and Information Technology (CVIT 2022), 2022, Beijing, China

Abstract

The fine-grained classification of remote sensing airplane images is a very meaningful work. Few of existing works have paid attention to the fine grained classification of remote sensing objects. The purpose of our research is to develop a better fine-grained classification performance of remote sensing airplane images. In this paper we propose a remote sensing airplane fine grained classification method with few shot learning. Few shot learning is used to alleviate the extreme imbalance distribution of the samples in different categories. We found two factors that affect the classification accuracy, the direction of the airplane and the background distribution. In order to increase the accuracy of classification and weaken the influence of the background, we propose an algorithm which use the symmetry of the image to predict the direction of the airplane, and add a Transpose encoder to alleviate the impact of the background distribution. The experimental results on Fair1m dataset proves the effectiveness of our method, which has obtained 5.73% up on accuracy performance.

Citation Download Citation

Xu Chen, HongBing Ma, and ShengJin Wang "RAFF: remote sensing airplane fine grained classification with few shot learning", Proc. SPIE 12590, Third International Conference on Computer Vision and Information Technology (CVIT 2022), 125900A (24 February 2023); https://doi.org/10.1117/12.2669648

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