Bulletin of Surveying and Mapping ›› 2024, Vol. 0 ›› Issue (10): 91-97.doi: 10.13474/j.cnki.11-2246.2024.1015.

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Comparison of terrain correction methods for high spatial resolution remote sensing images

WANG Yan1, LIU Yingjie1, WU Jinwen2,3, SUN Longyu4, LIU Jingnan5, XU Changhua6   

  1. 1. School of Transportation and Surveying Engineering, Shenyang Jianzhu University, Shenyang 110168, China;
    2. Shenyang Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China;
    3. Key Laboratory of Agricultural Meteorological Disasters in Liaoning Province, Shenyang 110166, China;
    4. Shenyang Meteorological Administration, Shenyang 110180, China;
    5. Huludao Meteorological Administration, Huludao 125080, China;
    6. Jinzhou Meteorological Administration, Jinzhou 121000, China
  • Received:2024-02-04 Published:2024-11-02

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Abstract: In mountainous areas with complex terrain, terrain shadows have a great impact on the extraction of remote sensing image information. Therefore, terrain correction should be carried out on remote sensing images to eliminate terrain effects and restore the surface reflectance of terrain shadow areas. This article takes the eastern forest area of Liaoning province (Liaodong Forest Area) as the research area, and uses GF-1 WFV remote sensing images with a spatial resolution of 16m. SCS+C, Minnaert+SCS and SCEDIL correction models are used to perform terrain correction on the original images. Visual analysis, spectral retention effect, terrain correction effect, classification accuracy verification and consistency of spectral reflectance on cloudy and sunny steep slopes are used to compare the images before and after correction, Finally determine the optimal terrain correction model suitable for forest areas. The research results indicate that: ①for forest areas with continuous mountainous and hilly terrain and significant undulations, SCS+C has better spectral retention compared to Minnaert+SCS and SCEDIL models, with a difference of less than 4.32 in the mean reflectance of each band before and after calibration, and there is no overcorrection phenomenon. The terrain correction effect of the three models is judged by the correlation between the corrected near-infrared reflectance and the cosine of the solar incidence angle. The SCS+C model has the smallest correlation, the best terrain correction effect, the Minnaert+SCS model has a slightly larger correlation and the SCEDIL model has overcorrection phenomenon. The image classification accuracy of the SCS+C model after correction has improved by nearly 3% compared to before correction, and is nearly 2% higher than the SCEDIL models of Minnaert+SCS. ②Based on the principle of terrain correction, a new evaluation method for the consistency of spectral reflectance on steep slopes of yin and yang has been added. The impact of NDVI on steep slopes of yin and yang before and after correction is used as the evaluation index for terrain correction effect. The SCS+C correction effect is the best, and the absolute deviation (10-2) of the mean spectral reflectance on steep slopes of yin and yang before and after correction in two typical areas is reduced from 1.14 to 0.58 and from 1.67 to 0.49, respectively. After correction, the consistency of steep slopes of yin and yang is improved. In summary, the SCS+C model is superior to Minnaert+SCS and SCEDIL, which is more suitable for terrain correction in forest areas.

Key words: topographic correction, GF-1, remote sensing, NDVI

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