In recent years, remote sensing images have been used for many different applications that require visual analysis and interpretation. In this paper, reducing/removing noise is the basic approach, as it causes loss of information and therefore affects the accuracy of the analyses. Within the scope of the study, two different test areas of land cover/use were applied to examine the effects of noise on optical satellite images. In this context, Landsat 8 and Sentinel 2 satellites were used to study the effects of denoising methods on different spatial resolutions. Due to the lack of raw images of the selected satellites, two different types of noise (i.e. Gaussian and Stripe) were added to the images. In this context, four different denoising methods were compared by using conventional filter techniques commonly used in the spatial domain, while also different methods that used different threshold values in the frequency domain. The first approach is Median, Block Matching and 3D Filtering methods in the spatial domain, applications that depend mainly on the neighborhood relationship of pixels in the image. The second approach is wavelet-based Contourlet and Curvelet methods in the frequency domain. The quality analysis of denoised images were evaluated as qualitative (statistical methods Peak Signal to Noise Ratio, Mean Square Error, standard deviation, min/max value), and quantitative. Finally, Curvelet hard thresholding transform was the selected method as the best algorithm after quality analysis additionally, the method also effectively preserves edges in homogeneous test area and other fine details in the heterogeneous test area.