地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (9): 1803-1816.doi: 10.12082/dqxxkx.2022.220188
吴亚楠1,2(), 郭长恩3, 于东平3, 段爱民3, 刘玉1, 董士伟1,*(
), 单东方1, 吴耐明4, 李西灿2
收稿日期:
2022-04-13
出版日期:
2022-09-25
发布日期:
2022-11-25
通讯作者:
*董士伟(1984— ),男,山东泰安人,高级工程师,博士,主要从事时空数据分析研究。 E-mail: dshiwei2006@163.com作者简介:
吴亚楠(1996— ),女,山东东营人,硕士研究生,主要从事土地利用变化与遥感应用研究。E-mail: 1224273284@qq.com
基金资助:
WU Yanan1,2(), GUO Chang'en3, YU Dongping3, DUAN Aimin3, Liu Yu1, DONG Shiwei1,*(
), SHAN Dongfang1, WU Naiming4, LI Xican2
Received:
2022-04-13
Online:
2022-09-25
Published:
2022-11-25
Contact:
DONG Shiwei
Supported by:
摘要:
空间分层是准确度量遥感分类不确定性程度及其空间分布的基础与关键。本文提出了一种基于不确定性分析的遥感分类空间分层及评估方法,首先基于随机森林算法获取像元后验概率,确定分类不确定性度量指标;其次,采用模糊C均值进行空间分层;最后,对分层结果合理性进行定性与定量评估,并与同尺度数据产品精度评价结果及后验概率不确定性分层方法进行对比分析。以北京市顺义区Landsat 8 OLI遥感影像数据为例,研究结果表明:① 基于最大概率、模糊混淆指数和概率熵指标将顺义区分为不确定性大、中、小3层,相应的遥感数据层分类精度分别为62.28%、74.96%、79.31%;② 分类不确定性空间分层结果与度量指标大小的空间分布基本一致,错分地类图层与不确定性大层的地类空间分布基本一致;③ 遥感数据和数据产品的各层地类空间特征、层分类精度大小趋势一致,与总体分类精度相比,不确定性大层的层分类精度降低,不确定性小层的层分类精度提高;④ 与后验概率不确定性分层方法相比,本研究不确定性大层的层分类精度降低1.08%,不确定性中层提高3.58%,不确定性小层提高0.16%,q值由0.19提高到0.24,空间分异性更高。证实了研发的遥感分类不确定性空间分层结果的合理性。研究旨在提出适用于遥感分类的不确定性分层方案,用于优化遥感分类训练样本和精度评价验证样本的空间布设。
吴亚楠, 郭长恩, 于东平, 段爱民, 刘玉, 董士伟, 单东方, 吴耐明, 李西灿. 基于不确定性分析的遥感分类空间分层及评估方法[J]. 地球信息科学学报, 2022, 24(9): 1803-1816.DOI:10.12082/dqxxkx.2022.220188
WU Yanan, GUO Chang'en, YU Dongping, DUAN Aimin, Liu Yu, DONG Shiwei, SHAN Dongfang, WU Naiming, LI Xican. Spatial Stratification and Evaluation Method of Remote Sensing Classification based on Uncertainty Analysis[J]. Journal of Geo-information Science, 2022, 24(9): 1803-1816.DOI:10.12082/dqxxkx.2022.220188
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