Denoising of high resolution remote sensing data using stationary wavelet transform

An image is often corrupted by a noise in its acquisition and transmission. A high resolution remote sensing data will be seen more roughly if it is corrupted by a noise. Wavelet is one of the fascinating denoising manners that will be used to solve this problem. The main application of the Stationary Wavelet Transform (SWT) is denoising. The principle is the average of several denoised signals. Each of them is obtained by using the usual denoising scheme, but it is applied to the coefficients of a e-decimated DWT. The stationary wavelet transform (SWT) is to make the wavelet decomposition time invariant. This improves the power of wavelet in the signal denoising. In this research, we apply the SWT method to preprocess the remote sensing data for removing the noise. The Worldview-1 satellite data is used in this research. The sensor resolution is 0.55 meters and Ground Sample Distance (GSD) at 20° off-nadir. The Area of Interest (Aol) is Monas, Jakarta and the acquisition of the data was done on March 13th, 2008. For the data analysis, the Worldview-1 satellite data is added by the noise. The result of this research is that the noise can be removed by SWT method. By using structural similarity index (SSIM), the quality of the denoised images by SWT, Wavelet Transform 2D and Wavelet Packet 2D are 0.2666, 0.1912, and 0.1927, respectively. Thus, the SWT provides a better performance in denoising the remote sensing data than Wavelet Packet 2 D and Wavelet 2D methods.