This paper presents a deep learning model based on an active learning strategy. The model achieves accurate identification of vegetation types in the study area by utilizing multispectral data obtained from preprocessing of unmanned aerial vehicle (UAV) remote sensing equipment. This approach offers advantages such as high data accuracy, mobility, and easy data collection. In active learning, the minimum confidence scoring method and a sampling technique based on a data pool are employed to reduce labeling costs. The deep learning model incorporates a semantic segmentation gated full fusion module that integrates a dual attention mechanism. This module enhances the capture of detailed texture information, optimally allocates spectral weights, and improves the model's ability to distinguish between similar categories. At a labeling cost of 20%, the average accuracy of the model is 93.2%. Compared with other models, the proposed model achieved the highest classification accuracy in the case of limited training samples. At full annotation cost, the average accuracy is 95.32%, with only a difference of about 2%, but saving 80% of annotation cost. Therefore, active learning strategies can filter out high-value samples that are beneficial for model training, greatly reducing the annotation cost of samples Finally, the recognition results of surface vegetation cover types in the study area are presented, and the model's accuracy is verified through field investigation.
Keywords: Convolutional block attention module; Convolutional neural network; Gated fully fusion; UAV remote sensing; Vegetation classification.
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