在监督分类的多光谱遥感图像中,比较了三种选择训练样本的方法
Hongying Zhang1, Jinxin He1, Shengbo Chen2
1College of Earth Sciences, Jilin University, Changchun 130061, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
分组选择方法是遥感图像分类的最佳方法,与基于和直接选择方法相比,在较少的训练样本中实现更高的准确性.
科学领域:
- 地球观测 地球观测
- 地理空间分析是什么
- 图像分类图像分类 图像分类
背景情况:
- 有效的训练样本选择对于准确的遥感图像分类至关重要.
- 为了选择培训样本,存在各种方法,每个方法都可能对分类性能产生影响.
研究的目的:
- 为了比较三个训练样本选择方法的有效性:分组选择,基于的选择和直接选择.
- 评估随机森林 (RF),支向量机 (SVM) 和k-最近邻居 (KNN) 分类模型的性能,使用这些选择方法在Sentinel-2,GF-1和Landsat 8图像上.
主要方法:
- 使用 Sentinel-2,GF-1 和Landsat 8 卫星图像.
- 实施了分组选择,基于的选择和训练数据的直接选择.
- 经过培训和评估的RF,SVM和KNN监督分类模型.
主要成果:
- 所有三个分类模型 (RF,SVM,KNN) 在评估的图像中都表现出类似的性能.
- 选择分组方法的分类准确度高于基于的选择,使用更少的样本.
- 当使用同等数量的样本时,分组选择也超过了直接选择.
结论:
- 在远程传感图像分类中的训练样本选择中,分组选择方法是优越的.
- 最佳的分类准确性是通过分组选择方法实现的,特别是在特定范围内的样本大小.
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