Optimal Minnaert topographic correction model based on land cover classification
- Vol. 26, Issue 12, Pages: 2542-2554(2022)
Received：30 September 2020，
Published：07 December 2022
DOI： 10.11834/jrs.20210399
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摘要
地形校正可以削弱地势复杂区域由于地形起伏导致的地表接收太阳辐射不均匀和地表反射率失真的问题，从而提升遥感影像质量和遥感信息提取的精度。但是，现有地形校正模型存在过校正、波段间校正效果不稳定以及校正效果不理想等问题。本文根据Minnaert地形校正模型系数
k
和地物二向性反射特性的相关性，对Minnaert模型进行改进，提出了一种考虑地物类型的Minnaert地形校正模型（简称为CMinnaert模型），并在地物预分类中采用《土地利用现状分类》一级分类标准和分植被疏密程度分类两种方式，用以验证CMinnaert模型的稳定性并筛选最佳地物类型划分方案。首先对待校正影像进行地物类型预分类，其次逐波段针对各地物类型分别进行系数
k
的拟合求解，然后使用各波段各地物类型的系数
k
对该范围的遥感影像进行Minnaert地形校正。以河南省商城县的Landsat 8/OLI影像为实验数据，选取余弦校正模型、SCS校正模型、Minnaert校正模型、分坡度的Minnaert校正模型作为对比模型，通过目视对比和统计数据分析的方式评估CMinnaert模型的地形校正效果。研究结果表明，本文提出的CMinnaert模型有效地削弱了地形效应对遥感影像辐射亮度值的影响，与原始影像和其他4种地形校正结果相比，进行地物一级分类的CMinnaert模型有效降低了各波段辐亮度与太阳入射角余弦的线性拟合
R
2
，未出现过校正现象；分植被疏密程度分类的CMinnaert模型在第1、5波段存在过校正问题，但其余波段辐亮度与太阳入射角余弦的线性拟合
R
2
是6种模型中最低的。以上结果证明两种地物预分类方式的CMinnaert模型校正效果都较稳定且明显优于其他四种地形校正模型，且本文建议在进行CMinnaert地形校正时采用地物一级分类的方式进行地物预分类。
Abstract
Topographic correction can reduce the problem of uneven solar radiation reception and surface reflectance distortion caused by terrain undulations in complex terrain areas
thus improving the quality of remote sensing images and the accuracy of remote sensing information extraction. However
existing topographic correction models have some problems
such as overcorrection
unstable effect of each band correction
and unsatisfactory correction accuracy.
This work proposes a corrected Minnaert topographic correction model
named the CMinnaert topographic correction model
which considers the type of land cover
based on the correlation between the
k
coefficient of the Minnaert topographic correction model and the bidirectional reflection characteristics of the ground object. Two methods are used in the pre classification of surface features: the first level classification of land cover types and the classification of vegetation density to verify the stability of the CMinnaert model. The best classification scheme of land cover types is proposed. First
a corrected image was pre-classified into land cover types
and the
k
coefficient was fitted to determine the land cover types in different places. Finally
Minnaert topographic correction was applied to the remote sensing data by using the
k
coefficient of the land cover type in each area. A Landsat 8/OLI image of Shangcheng County
Henan Province
China was used as experimental data.
The cosine correction model
the Sun Canopy Sensor (SCS) correction model
the Minnaert correction model
the Minnaert correction model based on slope
and the CMinnaert correction model were used to perform topographic correction of images in the research area. Visual comparison and statistical data analysis were used to evaluate the topographic correction performance of each algorithm. Results show that the CMinnaert correction model can effectively weaken the influence of the terrain effect on the radiance value of the remote sensing image: the CMinnaert correction model for the first level classification of land cover types can effectively reduce the linear fitting
R
2
of radiance and cosine of solar incidence angle at each band compared with the original image and the other four topographic correction results
and no over correction phenomenon occurred. Furthermore
the CMinnaert model of the vegetation density classification can effectively weaken the overcorrection problem of other correction models in bands 1 and 5. The linear fitting
R
2
of the radiance and cosine of the solar incidence angle in the other bands is the lowest of the six models. The CMinnaert model of two pre classification methods is more stable and better than the other four topographic correction models. The results of the visual comparison
cosine correlation analysis of the solar incidence angle
radiance histogram
and spectral characteristic analysis of the sunny and shady slopes are basically consistent.
This study recommends that the first level classification should be used in CMinnaert topographic correction considering the algorithm efficiency and practical application ability of the CMinnaert model.
关键词
Keywords
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CHEN Rui 西南交通大学 地球科学与工程学院
YIN Gaofei 西南交通大学 地球科学与工程学院
ZHAO Wei 中国科学院、水利部成都山地灾害与环境研究所
YU Huinan 西南交通大学 地球科学与工程学院
LIU Guoxiang 西南交通大学 地球科学与工程学院
Haiying JIANG 北京师范大学 地理科学学部 遥感科学国家重点实验室;北京师范大学 北京市陆表遥感数据产品工程技术研究中心
Kun JIA 北京师范大学 地理科学学部 遥感科学国家重点实验室;北京师范大学 北京市陆表遥感数据产品工程技术研究中心
Xiang ZHAO 北京师范大学 地理科学学部 遥感科学国家重点实验室;北京师范大学 北京市陆表遥感数据产品工程技术研究中心
相关机构
Faculty of Geosciences and Engineering, Southwest Jiaotong University
Institute of Mountain Hazards and Environment, Chinese Academy of Sciences
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University
Beijing Engineering Research Center for Global Land Remote Sensing Products, Beijing Normal University
Aerospace Information Research Institute, Chinese Academy of Sciences
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