Abstract: In traditional machine learning, the accuracy of the model is often determined by the size of the labeled data sample. However, in the actual situation, only a small part of the massive data is usually accurately marked, while most of the data is not marked. If the data is marked one by one by professionals, it will cost a lot of time and economic costs. Active learning is to retrieve the most useful unlabeled data from a large number of unlabeled data sets, hand it over to professionals for labeling, and then train the model with such samples so as to improve the accuracy of the model. This paper designs a target detection method of remote sensing images. Firstly, a deep learning network model is constructed and pretrained by using the labeled data. This process is iterated repeatedly until the accuracy reaches the set threshold. In the experiment, the labeled data account for 14.2%, 21.4% and 28.6% of the total data respectively. The experimental results show that this method of combining active learing with U-Net network can effectively reduce the amount of data labeling so as to achieve the expected effect of the model.

Key words: active learning, target detection, remote sensing image, land classification