Remote sensing image near-infrared band terrain shadow correction method for monitoring mountain land

Abstract

The invention provides a remote-sensing image near-infrared band terrain shadow correction method for monitoring mountainous regions, and the method comprises the following steps: S1, obtaining satellite remote-sensing data and DEM data of a research region, and carrying out the data preprocessing; s2, extracting a terrain shadow by adopting machine learning and a water body index, namely, NDWI; s3, performing SCS + C terrain correction by adopting the surface reflectance after atmospheric correction; s4, calculating a terrain shadow elimination vegetation index, namely SEVI, by adopting the surface reflectance; s5, carrying out red light wave band terrain shadow correction; and S6, carrying out near-infrared band terrain shadow correction. By applying the technical scheme, the interference of the terrain shadow on the near-infrared band of the remote sensing image can be effectively eliminated, and the defect that the conventional terrain correction method based on the DEM is poor in near-infrared band terrain shadow correction effect, especially the defect of shadow failure is overcome.

Description

technical field [0001] The invention relates to the technical field of terrain shadow compensation, in particular to a terrain shadow correction method in the near-infrared band of remote sensing images used for monitoring mountains. Background technique [0002] Terrain shadows seriously interfere with the accuracy of monitoring, classification and extraction of terrain features in mountainous areas, and cause great difficulties in the interpretation of land cover in mountainous areas. Therefore, it is of great significance to study the spectral information compensation method of terrain shadows. The current terrain shading correction methods mainly include empirical correction model based on DEM data, physical correction model and band combination optimization calculation model (vegetation index). The empirical correction method based on DEM data mainly considers direct solar radiation, and performs empirical correction based on the statistical relationship between reflect...

AI Technical Summary

Problems solved by technology

The empirical correction method based on DEM data mainly considers direct solar radiation, and performs empirical correction based on the statistical relationship between reflectivity and cos i, including statistical-empirical model, normalization model, C correction, SCS correction, etc., but such methods Sunny slope correction effect is good, but terrain shading correction effect is limited, especially fall shadow correction effect is not good

The physical correction model considers direct solar radiation, atmospheric diffuse radiation, and surrounding terrain reflection radiation at the same time, and the effect is good, but the model has many parameters and the calculation process is complicated, so it is difficult to popularize

The method based on the band combination optimization calculation model obtains the vegetation information after eliminating the influence of terrain by constructing a special vegetation index, such as the band ratio model, VBSI index, terrain-adjusted vegetation index (TAVI), normalized difference mountain vegetation index, improved Enhanced Vegetation Index, Shadow Elimination Vegetation Index (SEVI), etc. However, this type of model can only obtain index information of a single band and lacks multispectral information

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