地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (7): 1275-1285.doi: 10.12082/dqxxkx.2022.210704
收稿日期:
2021-11-03
修回日期:
2022-01-17
出版日期:
2022-07-25
发布日期:
2022-09-25
通讯作者:
* 伍宇明(1989—),北京人,男,博士,副研究员,主要从事地质灾害研究,E-mail: wuym@lreis.ac.cn作者简介:
刘 佳(1993—),男,河北沧州人,博士研究生,主要研究方向为滑坡识别与动力学模拟,E-mail: liuj.19b@igsnrr.ac.cn
基金资助:
LIU Jia1,3(), WU Yuming1,*(
), GAO Xing1, SI Wentao2
Received:
2021-11-03
Revised:
2022-01-17
Online:
2022-07-25
Published:
2022-09-25
Contact:
WU Yuming
Supported by:
摘要:
地震滑坡解译是震后重建的重要基础工作,主要通过室内人工遥感解译和室外野外调查确定。地震滑坡相比其他地物来说更为复杂,很难通过简单指数识别。室内遥感解译通过滑坡后壁、侧壁和堆积等纹理特征进行识别,大面积同震滑坡解译工作往往耗费大量人力和物力,且耗时长,难以满足灾害应急需求。本研究利用U-net神经网络模型,结合Google Earth Engine(GEE)云平台和人工智能学习平台Tensorflow,以地震局解译的汶川滑坡作为样本数据,以震后30 m分辨率的Landsat影像、高程、坡度以及NDVI数据作为模型输入参数,自动识别并获取了汶川地震后的同震滑坡数据,同时比较了不同参数组合情况下U-net神经网络模型的分割识别精度。研究表明:① U-net模型可以用于以Landsat影像为基础数据的同震滑坡快速自动识别;② 随着高程、坡度以及NDVI等输入参数增加,模型分割精度在逐渐提高,但假阳性结果也会出现增多,震后滑坡影像+高程+坡度+NDVI的输入参数组合精度最高;③ 在细节上,模型在多参数组合的情况下,大型滑坡能够很好被识别,一些较小型的滑坡受制于影像分辨率的影响,分割精度较差。为了更好识别小型滑坡,后续研究可能需提高影像的分辨率。此外,GEE云平台大大提高了训练样本获取的效率,为科研人员快速进行基于神经网络与遥感数据的地物识别研究提供了条件。
刘佳, 伍宇明, 高星, 司文涛. 基于GEE和U-net模型的同震滑坡识别方法[J]. 地球信息科学学报, 2022, 24(7): 1275-1285.DOI:10.12082/dqxxkx.2022.210704
LIU Jia, WU Yuming, GAO Xing, SI Wentao. Image Recognition of Co-seismic Landslide based on GEE and U-net Neural Network[J]. Journal of Geo-information Science, 2022, 24(7): 1275-1285.DOI:10.12082/dqxxkx.2022.210704
表2
不同参数组合情况下不同地区的混淆矩阵
A区实际 | B区实际 | C区实际 | |||||||
---|---|---|---|---|---|---|---|---|---|
P | N | P | N | P | N | ||||
方案1预测 | P | 4731 | 1774 | 9836 | 4211 | 5232 | 2572 | ||
(LT05) | N | 8929 | 50 102 | 14 603 | 36 886 | 7115 | 50 617 | ||
方案2预测 | P | 4022 | 1337 | 10 669 | 4830 | 6579 | 2960 | ||
(LT05+DEM+SLOPE) | N | 9638 | 50 539 | 13 770 | 36 267 | 5768 | 50 229 | ||
方案3预测 | P | 4868 | 1796 | 12 141 | 6060 | 7066 | 3241 | ||
(LT05+DEM+SLOPE+NDVI) | N | 8792 | 50 080 | 12 298 | 35 037 | 5281 | 49 948 |
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