一个包容性的分类优化模型,用于土地使用和土地覆盖分类
Li Ma1,2, Xuan Li3, Jianwei Hou4
1School of Earth Science and Engineering, Hebei University of Engineering, Handan, 056038, China. mali@hebeu.edu.cn.
Scientific reports
|March 22, 2025
概括
包容性分类优化模型 (ICOM) 通过融合多个数据源和分类器来改进土地使用分类. 这种先进的模型提高了大规模绘图的整体和本地分类准确性.
科学领域:
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
- 机器学习用于地球观测
背景情况:
- 准确的土地使用和土地覆盖 (LULC) 分类对于环境监测和资源管理至关重要.
- 现有的LULC分类方法往往在大规模应用和有效整合多种数据源方面扎.
研究的目的:
- 引入和评估包括分类优化模型 (ICOM) 进行先进的大规模LULC分类.
- 开发一种可靠的方法,自动导出和完善培训和验证样本.
- 整合多个分类结果以提高准确性和可靠性.
主要方法:
- 开发了用于自动化LULC分类的包括分类优化模型 (ICOM).
- 采用了五个顶级分类产品的交叉和联合区域,用于样本导出.
- 采用调和指数来完善样本和优化Landsat图像复合方法.
- 整合了六个谷歌地球引擎分类器和11个分类结果,使用准确度加权的多元投票.
- 整合了传统的 (制造商,用户) 和新的匹配准确度指数,用于性能评估.
主要成果:
- ICOM有效地利用了多个高质量的分类产品和分类器的优势.
- 综合分类输出显示了整体准确度的显著提高.
- 局部分类性能得到了显著的改善,更准确地反映了现实世界的情况.
结论:
- 通过融合各种数据和方法,ICOM提供了一个强大的框架来优化大规模的LULC分类.
- 该模型的自动样本精细化和准确度加权集成导致了优异的分类结果.
- 对于环境应用而言,ICOM在制作可靠和详细的LULC地图方面取得了重大进展.
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