Topographic Correction (TC) is one of the essential pre-processing methods to reduce the topographic effect in remote sensing data. It is one of the main factors affecting the reflectance value of objects in remote sensing imagery in the rugged topographic area and contributes to quantitative analysis. High-resolution satellite imagery also requires high spatial resolution of the Digital Elevation Model (DEM) as an important requirement in applying TC methods. This study evaluated the performance of five different TC algorithms (i.e., Statistical-Empirical (SE), C-Correction, Minnaert, Gamma, and Sun Canopy Sensor+C (SCS+C) over four hilly to the undulating area with different land-cover characteristics on SPOT-6/7 Multispectral imagery in Sulawesi Island and used the nation-wide DEMNAS as DEM. Visual and statistical evaluations were used to examine the surface reflectance value before and after correction by calculating linear regression and Pearson Correlation (R) between illumination (IL) and reflectance value, and the difference between mean reflectance value of lit and shadowed for vegetated slope. The results showed that the Minnaert, SCS+C, and C-Correction, perform better than other methods. However, Minnaert and SCS+C statistically and visually performed better in all topographic conditions, and C-Correction showed moderate performance. The Gamma method tends to be under-correction but is visually suitable for favorable topographic conditions and poorly illuminated areas in shaded slopes area. In contrast, SE tends to overcorrect all SPOT-6/7.
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