Xie, G.; Liu, P.; Chen, Z.; Chen, L.; Ma, Y.; Zhao, L. Time-Series Remote Sensing Image Classification with Active Deep Learning. Preprints2024, 2024101526. https://doi.org/10.20944/preprints202410.1526.v1
APA Style
Xie, G., Liu, P., Chen, Z., Chen, L., Ma, Y., & Zhao, L. (2024). Time-Series Remote Sensing Image Classification with Active Deep Learning. Preprints. https://doi.org/10.20944/preprints202410.1526.v1
Chicago/Turabian Style
Xie, G., Yan Ma and Lingjun Zhao. 2024 "Time-Series Remote Sensing Image Classification with Active Deep Learning" Preprints. https://doi.org/10.20944/preprints202410.1526.v1
Abstract
Deep learning methods have been widely applied to time series classification tasks. Although deep learning methods perform excellently on these tasks, they require much training data. Labeling time series data for training is very time-consuming and labor-intensive. Active learning can be used to select the most informative data for labeling in time series classification tasks to save human labeling efforts. In this paper, we propose a new active learning (AL) framework for time series data. First, a temporal classifier for pixel-level temporal classification tasks is designed. Next, We propose an effective active learning method to select informative time series samples for labeling, which combines representativeness and uncertainty. For representativeness, We use the K-shape method to cluster time series data. For uncertainty, we construct an auxiliary deep network to evaluate the uncertainty of unlabeled data. The features of the middle hidden layer with rich temporal information of the classifier will be fed into the auxiliary deep network, which is equipped with a self-attention mechanism to utilize the temporal dependencies of time series data fully. Then, we define a new loss function with the aim of improving the deep model’s performance. Finally, the proposed method in this paper was evaluated on two temporal remote-sensing image datasets. The results demonstrate a significant advantage of our method over other approaches to time series data. Code is available at https://github.com/Fighting-Golion/time_series_active_learning
Keywords
Time series; active learning (AL); image classification
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
