Today satellite images are extensively considered in different fields of research. But the main problem associated with satellite images is their resolution. Hence, we propose a method to resolve the resolution problems associated with satellite images with transform domain methods such as Discrete Wavelet Transform, Dual-Tree Complex Wavelet Transform and Discrete Wavelet Transform with Stationary Wavelet Transform methods. Wavelet transform decomposes the low-resolution input image into four different subband images such as Low–Low, Low–High, High–Low, and High–High. Then Bicubic interpolation is applied on subband to resize the subband images and to get estimated images. All the estimated images and low-resolution images are combined by using Inverse Discrete Wavelet Transform to obtain a high-resolution image. All the methods are compared with different satellite images. It is observed that Discrete Wavelet Transform with Stationary Wavelet Transform maintains the high-frequency components due to interpolation applied to subband images. It preserves sharpness and details of high-frequency components in the images. Direction selectivity is also very good in Discrete Wavelet Transform with Stationary Wavelet Transform. This provides better results as compared to other methods. The results are evaluated for quantitative peak signal-to-noise ratio, Root Mean Square Error, Mean Square Error, Mean Absolute Error, and Time to prove the supremacy.