Abstract
6G can connect everything, including aviation equipment. Aviation equipment transmits remote sensing images through 6G network to obtain ground information which can effectively help users analyze geographical types, ground conditions, etc. Recently, deep learning methods have made significant breakthroughs in remote sensing image classification. However, it takes a lot of human resources to add labels to the data. In this article, we design a new semi-supervised image classification framework for remote sensing scenarios. This framework uses pseudo-labels as labels of unlabeled data, so that unlabeled data can also be trained with labels. We provide a hybrid representation learning method for the case that the model may misclassify unlabeled data. Mixing different data to generate pseudo data and taking advantage of all the data can overcome the shortcomings of pseudo labels. We use the NWPU-RESISC45 dataset provided by Northwestern Polytechnical University, from which we randomly select ten-class samples for evaluation. The experimental results show that our proposed method is superior to the comparative methods.
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This work was supported by Heilongjiang Province Natural Science Foundation under Grant LH2022F034.
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Meng, X., Xi, L., Liu, L. (2023). A Semi-supervised Classification Method for 6G Remote Sensing Images Based on Pseudo-label and False Representation Recognition. In: Li, A., Shi, Y., Xi, L. (eds) 6GN for Future Wireless Networks. 6GN 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-031-36014-5_2
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