时间序列遥感图像分类使用数据驱动的主动深度学习方法
Gaoliang Xie1,2, Peng Liu1, Zugang Chen1
1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
这项研究引入了一个活跃的深度学习框架,以有效地标记时间序列遥感图像用于土地利用地图. 该方法通过智能选择信息样本,显著提高了分类准确性,减少了人工标签工作.
科学领域:
- 地球和环境科学 地球和环境科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 时间序列遥感图像 (TSRSI) 对于土地使用/土地覆盖 (LULC) 地图制作至关重要.
- 深度学习擅长处理时间数据,但需要广泛的标记样本.
- 手动标记TSRSIs是耗时和劳动密集的.
研究的目的:
- 为TSRSI分类开发一个活跃的深度学习框架.
- 为了应对TSRSI分析中有限的标记数据的挑战.
- 为了减少人类在标记大规模遥感数据集方面的努力.
主要方法:
- 为TSRSI分类提出了一个数据驱动的主动深度学习框架.
- 设计了一个时间分类器和一个积极学习策略,考虑代表性 (K形集群) 和不确定性 (辅助深度网络).
- 引入了一种新的损失函数,以提高深度模型性能.
主要成果:
- 在多个TSRSI数据集 (MUDS,DynamicEarthNet,PASTIS) 上取得了显著的准确性改进.
- 证明了实质性的收益,例如,在有限的初始样本中,DynamicEarthNet的准确性提高了7.81%.
- 提出的积极学习方法有效地识别信息样本,优于其他方法.
结论:
- 开发的主动深度学习框架为TSRSI分类提供了一个有效的解决方案,使用有限的标记数据.
- 该方法有效地平衡了样本代表性和不确定性,以实现最佳的积极学习.
- 这种方法大大降低了与大规模遥感数据标签相关的成本和工作量.
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