基于高分辨率卫星遥感影像滑坡提取方法研究现状

Research Status of Landslide Extraction Methods based on High-resolution Satellite Remote Sensing Images

滑坡具有强大的爆发力和破坏性,是世界上发生频率较高的自然灾害之一,给人们的生命财产造成了严重的损害。灾后准确快速的提取滑坡,获取滑坡的分布范围,对滑坡灾害调查及危险性评估极为重要。围绕基于高分辨率卫星遥感影像监测滑坡的方法进行了调研,首先介绍了滑坡在高分辨率卫星遥感影像上的解译特征,而后论述了滑坡提取方法和精度评价分析方法的研究进展,最后总结了当前方法的优势与不足,以及未来研究的发展方向。结果表明:深度学习方法具有较大的潜力,未来应加强深度学习与其他自动化解译方法的结合在滑坡监测中的应用,解决样本规模对模型结果的影响,实现模型的可迁移性,提高其自动化程度。

Landslides are powerfully explosive and destructive,and are one of the natural disasters with high frequency in the world,causing serious damage to people's lives and properties.Accurate and rapid extraction of landslides and obtaining the distribution range of landslides after a disaster are extremely important for landslide disaster investigation and hazard assessment.The method of landslide monitoring based on high-resolution satellite remote sensing images is investigated.Firstly,the decoding characteristics of landslides on high-resolution satellite remote sensing images are introduced,while the research progress of landslide extraction methods and accuracy evaluation and analysis methods are discussed,and finally the advantages and shortcomings of current methods are summarized,as well as the development direction of future research.The results show that the deep learning method has greater potential,and the combination of deep learning and other automated interpretation methods should be strengthened in landslide monitoring in the future to solve the influence of sample size on the model results,realize the migrability of the model,and improve its automation.