• Journal of Soil and Groundwater Environment
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• v.22 no.6
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• pp.120-128
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• 2017

The aim of this study is to analyze the accuracy of SRTM Ver 3.0 and ASTER GDEM Ver 2 over Korea. To enable this, accuracy analysis was performed by using precise DEM which was made with multiple aerial image matching and national base map benchmark. The result of this study identified both SRTM and ASTER have different features. The height of the SRTM was found to be higher (3.8 m on average) at lower elevation and lower (8.4 m on average) at higher elevation. In contrast, the ASTER was found to be lower than the actual height at both lower and higher elevation (2.92 m, 4.51 m on average). The cause of this height bias according to the elevation is due to the differences in data acquisition and processing methods of DEM. It was identified however that both SRTM and ASTER were within allowable limits of error. In addition, RMSE of the SRTM was smaller than the ASTER in comparison to benchmark, and also the bias trend both at higher and lower terrain were similar to the precise DEM which was made with multiple aerial image matching. Therefore, the reliability of SRTM can be considered to be higher.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.728-735
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• 2017

With the proliferation of digital devices, the devices have generated considerable additive white Gaussian noise while acquiring digital images. The most well-known denoising methods focused on eliminating the noise, so detailed components that include image information were removed proportionally while eliminating the image noise. The proposed algorithm provides a method that preserves the details and effectively removes the noise. In this proposed method, the goal is to separate meaningful detail information in image noise environment using the edge strength and edge connectivity. Consequently, even as the noise level increases, it shows denoising results better than the other benchmark methods because proposed method extracts the connected detail component information. In addition, the proposed method effectively eliminated the noise for various noise levels; compared to the benchmark algorithms, the proposed algorithm shows a highly structural similarity index(SSIM) value and peak signal-to-noise ratio(PSNR) value, respectively. As shown the result of high SSIMs, it was confirmed that the SSIMs of the denoising results includes a human visual system(HVS).