准确地分类土地使用和土地覆盖,使用特定于边界的两级学习方法,并增加了谷歌地球引擎中的辅助功能
Rohini Selvaraj1, Geraldine Bessie Amali D2
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, India.
Environmental monitoring and assessment
|October 7, 2023
概括
这项研究通过将辅助功能与一种新的两级学习方法相结合,提高了印度马杜莱的土地使用土地覆盖面 (LULC) 分类准确性. 这种方法对于监测土地退化和保护该地区至关重要.
科学领域:
- 遥感和地理空间分析
- 环境监测 环境监测
- 城市规划 城市规划
背景情况:
- 土地使用 土地覆盖 (LULC) 分类对于环境监测,气候变化研究和城市发展至关重要.
- 印度的马杜莱面临着土地退化影响其地理和文化遗产,包括传统运动Jallikattu.
- 监测LULC变化对于可持续的土地管理和遗产保护至关重要.
研究的目的:
- 通过使用遥感数据,提高马杜拉里的LULC分类准确度.
- 将光谱,纹理和辅助特征与先进的分类算法集成在一起.
- 评估LULC映射的边界特定的两级学习方法的有效性.
主要方法:
- 使用Landsat 8卫星图像进行LULC分类.
- 使用简单的非代聚类 (SNIC) 来进行图像细分.
- 实施了特定于边界的两级学习方法,使用支持矢量机 (SVM) 和K-近邻 (kNN) 增强功能.
主要成果:
- 通过提出的方法获得了95.78%的整体准确率 (OA) 和0.94的卡帕分数 (K).
- 与传统的分类方法 (PB,OB,OBS) 相比,显著改进.
- 证实了整合辅助功能和两级学习策略的有效性.
结论:
- 特定于边界的两级学习方法显著提高了LULC分类的准确性.
- 用辅助数据增加光谱和纹理特征可以提高分类性能.
- 这种精细的LULC分类有助于有效地监测土地退化和马杜莱的可持续城市规划.
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