用变压器网络对草原进行多时空的超谱分类
Xuanhe Zhao1, Shengwei Zhang2, Ruifeng Shi3
1College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China.
Sensors (Basel, Switzerland)
|July 29, 2023
概括
一个新的变压器网络 (MHCgT) 准确地使用多时间超光谱数据对草原物种进行分类. 这种方法达到98.51%的准确性,改善了可持续的草原管理和物种多样性评估.
科学领域:
- 遥感 遥感 遥感 遥感
- 生态生态学 生态生态学
- 计算机科学 计算机科学
背景情况:
- 草原监测传统上依赖于现场调查,但遥感方法提供了更大的可扩展性.
- 现有的遥感技术在草原分类中难以达到所需的准确性.
- 多时间超谱数据具有显著的潜力,可以区分草原物种和生长阶段.
研究的目的:
- 探索变压器网络的应用,以分析草原分类中的多时间超光谱数据.
- 通过使用变压器网络 (MHCgT) 引入和评估一种新的草原多时空高光谱分类.
- 提高高功率草原检测和物种识别的准确性.
主要方法:
- 从不同生长阶段 (400-1000 nm) 的草原样本中收集了16800个多时间超谱数据点.
- 开发了MHCgT网络,具有层次架构和多头自我注意机制,用于特征提取.
- 对最先进的方法 (CNN,LSTM-RNN,SVM,RF,DT) 进行了废除研究和比较实验.
主要成果:
- 拟议的MHCgT框架在多时间超光谱草原识别中实现了98.51%的高精度.
- MHCgT的性能比现有的方法高出6.42-26.23%.
- 平均物种分类准确度超过95%,成熟阶段 (八月) 比生长阶段 (六月) 更容易识别.
结论:
- 该MHCgT框架显示了精确的多时间超频谱物种识别的巨大潜力.
- 这种方法为可持续的草原管理和物种多样性评估提供了有价值的应用.
- 变压器网络是从复杂的超光谱时间序列数据中提取特征的强大工具.
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