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Shadow-aware terrain correction for karst landforms using remote sensing data
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AI
DOI：10.1080/01431161.2025.2573242.png)
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Abstract
En 中文
Terrain correction is crucial in enhancing the quality of remote sensing imagery in mountainous regions by mitigating terrain-induced distortions. Despite advancements, current methods fall short in effectively addressing terrain shading issues. The Semi-empirical Method Considering Shadow (SMCS) was developed in this study, and the performance of SMCS, SCS, and SCS+C models on images of varying spatial resolution in karst landscapes was also evaluated. The results demonstrate that the SMCS method effectively enhances remote sensing images of complex mountainous areas. Compared with the traditional terrain correction method, the SMCS method significantly improves the correction effect. The R2, which reflects the relationship between solar incidence angle and image reflectance, decreased from 0.489, 0.037, and 0.067 to 0.046, 0.018, and 0.022, respectively. This indicates that the SMCS method effectively weakens the linear correlation between image reflectance and solar incidence angle. Additionally, the IQRR across bands confirms the SMCS method’s ability to correct spectral discrepancies among similar features. The SMCS model demonstrates robust performance across all tested resolutions, confirming its broad applicability and effectiveness under varying spatial scales. Furthermore, the study highlights that terrain-induced effects become increasingly significant at higher resolutions, posing substantial challenges to conventional correction approaches.
Keywords:
SMCS method
terrain correction
karst landforms
high resolution
Journal
IF:
2.6
Papers:
1.2W
Citations:
2.7W
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