在随机森林模型中使用GLCM纹理特征在Sentinel 2图像中增强主动火灾检测
Bao Zhou1, Sha Gao2, Ying Yin1
1College of Electronic and Information Engineering, West Anhui University, Luan, 237000, China.
Scientific reports
|December 27, 2024
概括
人类造成的野火严重污染了大气. 这项研究使用 Sentinel-2 数据和纹理特征开发了一个优化的随机森林 (RF) 模型,在中国多样化的景观中实现了86.1%的准确性,用于积极检测火灾.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 林业林业 林业 林业 林业
背景情况:
- 人类活动是野火的主要原因,导致大气污染和公共安全风险.
- 精确检测活跃火灾点对于减轻野火影响至关重要.
研究的目的:
- 开发和优化一个随机森林 (RF) 模型,以使用 Sentinel-2 卫星数据增强主动火点检测.
- 评估光谱和纹理特征对提高火灾检测准确性的贡献.
主要方法:
- 利用 Sentinel-2 卫星数据,在中国的各种土地覆盖类型中进行积极的火灾检测.
- 采用了光谱指数方法,值和随机森林 (RF) 模型.
- 使用吉尼系数评估特征重要性,并纳入灰色级别共发生矩阵 (GLCM) 纹理特征.
主要成果:
- 灰色级别共发生矩阵 (GLCM) 纹理特征被确定为至关重要的,占最终特征集的40%,显著提高了检测准确性.
- 优化的射频模型实现了86.1%的整体准确性,用于积极的火灾检测.
- 该研究证明了定制射频模型在中国各种土地覆盖环境中的有效性.
结论:
- 优化的随机森林 (RF) 模型,结合了GLCM纹理特征,提供了一种强大而准确的方法来进行主动火灾检测.
- 这种方法适用于中国各地的各种土地覆盖类型,有助于野火管理和大气污染监测.
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