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引用本文:

DOI:

10.11834/jrs.20210399

收稿日期:

2020-09-11

修改日期:

2021-06-12

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考虑地物类型的Minnaert地形校正模型优化方法
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(CMinnaert校正模型), 并在地物预分类中采用地物一级分类和分植被疏密程度分类两种方式, 用以验证CMinnaert模型的稳定性并提出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对该范围的遥感数据进行Minnaert地形校正。以河南省商城县的Landsat 8/OLI影像为实验数据, 分别利用余弦校正模型、SCS校正模型、Minnaert校正模型、分坡度的Minnaert校正模型和CMinnaert校正模型对研究区影像进行地形校正, 通过目视对比和统计数据分析的方式, 评估各算法性能及地形校正效果。研究结果表明, 本文提出的CMinnaert校正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:与原始影像和其他四种地形校正结果相比, 进行地物一级分类的CMinnaert校正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R2,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波段有效削弱了其他校正模型的过校正问题, 其余波段辐亮度与太阳入射角余弦的线性拟合R2是六种模型中最低的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; 地物一级分类的CMinnaert模型与分植被疏密程度的CMinnaert模型目视比较、太阳入射角余弦相关性分析、辐亮度直方图和阴阳坡光谱特征分析结果基本一致, 考虑到CMinnaert模型的算法效率和实际应用能力, 本文建议在进行CMinnaert地形校正应用时采用地物一级分类的方式。

Optimization method for a Minnaert topographic correction model considering land cover types
Abstract:

[Objective] 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, there are some problems with existing topographic correction models, such as overcorrection and the fact that the effect of each band correction is not stable and the correction accuracy is not ideal. [Method] Based on the correlation between the k coefficient of the Minnaert topographic correction model and the bidirectional reflection characteristics of the ground object, this paper proposes a corrected Minnaert topographic correction model, named the CMinnaert topographic correction model, which considers the type of land cover. In the pre classification of surface features, two methods are used: the first level classification of land cover types and the classification of vegetation density to verify the stability of CMinnaert model, and the best classification scheme of land cover types is proposed. Firstly, 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, were used as experimental data. [Result] 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 respectively 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. The results show that the CMinnaert correction model can effectively weaken the influence of terrain effect on the radiance value of remote sensing image: compared with the original image and the other four topographic correction results, the CMinnaert correction model for the first level classification of lands cover types can effectively reduce the linear fitting R2 of radiance and cosine of solar incidence angle at each band, and there is no over correction phenomenon; The results show that the CMinnaert model of vegetation density classification can effectively weaken the overcorrection problem of other correction models in the band 1 and 5, the linear fitting R2 of radiance and cosine of 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 visual comparison, cosine correlation analysis of solar incidence angle, radiance histogram and spectral characteristics analysis of sunny slope and shady slope are basically consistent. [Conclusion] Considering the algorithm efficiency and practical application ability of CMinnaert model, it is suggested that the first level classification should be used in CMinnaert topographic correction.

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