In the application of unmanned aerial vehicle (UAV), land classification is an important field. Image recognition technology based on deep learning is a new method to solve the problem of land classification in recent years. In this paper, we propose a land classification method based on federated learning (FL) which uses Fedavg-Adam algorithm. This method enables UAVs to learn land models using training data distributed in their local database. By aggregating local computational updates of the land classification model, a shared global model is constructed. UAVs can collectively benefit from global models without sharing datasets and protect sensitive information collected by UAVs. To obtain good classification performance, we further propose an improved CNN network. The experimental results show that the improved CNN network suitable for federated learning has the good performance considering the influence of time and accuracy. In the RSSCN7 Dataset, the FedAvg-Adam algorithm converges in the 43rd rounds with an accuracy rate of 82.71%. Compared with FedAvg and FedAdam, the final accuracy of the three is similar, but the latter two converge at 56 rounds and 114 rounds respectively, and Fedavg-Adam has the fastest speed, which proves the superiority of our method.

Kun Yao, Xinyue Cao. UAV Land Classification Method Based on Federated Learning. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 7: 73-78. https://doi.org/10.25236/AJCIS.2022.050712.

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