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Optimal Minnaert topographic correction model based on land cover classification

  • role： First author第一作者
  • Affiliation：

    School of Computer and Information Engineering, Henan University, Kaifeng 475000, China

    Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China

  • Email：linyh@henu.edu.cn
  • Introduction：林英豪，研究方向为植被定量遥感应用。E-mail： linyh@henu.edu.cn
LIN Yinghao12,  
  • Affiliation：

    School of Computer and Information Engineering, Henan University, Kaifeng 475000, China

    Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China

JIN Yan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Computer and Information Engineering, Henan University, Kaifeng 475000, China

    Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China

  • Email：shenxj@henu.edu.cn
  • Introduction：沈夏炯，研究方向为空间数据分析、分布/并行计算、形式概念分析。E-mail： shenxj@henu.edu.cn
SHEN Xiajiong12*,  
  • Affiliation：

    School of Computer and Information Engineering, Henan University, Kaifeng 475000, China

    Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng 475000, China

ZHOU Liming12

résumé

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 R2 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 R2 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.

mots-clés

remote sensing;topographic correction;Minnaert model;land cover type;Landsat 8/OLI

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