刘冰洁,邢旭东,吴浩,胡少华,昝军

• 1:华中师范大学城市与环境科学学院
• 2:中南电力设计院有限公司
• 3:武汉理工大学安全科学与应急管理学院
• 4:湖北省安全生产应急救援中心

摘要(Abstract)：

针对现有安全风险防范工作中对尾矿库数量统计与"一库一策"的管理需求,该文提出了一种将深度学习与随机森林结合的典型尾矿库遥感目标识别方法。首先,运用Faster R-CNN模型进行基于遥感影像的尾矿库目标检测,实现大范围影像内尾矿库正确识别结果的保留;其次,为进一步提高识别精度,统计识别结果中图像的几何特征,采用U-Net分别设计尾矿库语义分割模型与尾矿库水、砂、坝场景分割模型,获取了尾矿库面积特征、场景元素几何特征与空间关系特征,通过改进的Res-Net图像分类模型得出尾矿库概率特征;最后,基于随机森林分类模型对单一特征与多特征组合进行模型训练与测试,得到最优特征参数组合,从而实现典型尾矿库的高精度识别。结果表明,该文提出的方法能够在稀少样本条件下,通过特征提取与最优特征组合实现不同区域尾矿库高精度识别与提取,可为尾矿库科学管理工作提供一定技术支持。

关键词(KeyWords)： 遥感影像;目标检测;特征提取;随机森林;尾矿库识别

Abstract:

Keywords:

基金项目(Foundation): 湖北省技术创新专项(重大项目)(2019ACA143)

作者(Author): 刘冰洁,邢旭东,吴浩,胡少华,昝军

DOI: 10.16251/j.cnki.1009-2307.2021.12.018

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