Speckle noise reduction of Sentinel-1 SAR data using fast fourier transform temporal filtering to monitor paddy field area

Abstract

Synthetic Aperture Radar (SAR) images have the ability to work in any weather situation. It is mostly impossible to get cloud free optical image monthly in Indonesia, especially in Subang. So, the use of SAR imagery for the monthly monitoring of paddy is recommended. The disadvantage of the SAR images is noise, known as speckle noise. This noise reduces the quality of the image; reducing the speckle noise is necessary. This research proposes the FFT algorithm to remove the speckle noise. Because the frequency pattern of FFT is periodic, and the research focuses on the paddy field area, so the one year of paddy growth was used. There are many planting times in the research area, from one to three planting times a year; most of the area is twice planting time a year. Six scenarios of the FFT filter were proposed and investigated to select the optimum scenario. The scenarios use the number of FFT results to implement in filtering. The first scenario used FFT1 to FFT2, and the sixth scenario used FFT-1 to FFT-8. The performance results were measure by the correlation value of the original image and the results of FFT filtering. The results show that by increasing the number of FFT in the filtering process, the correlation increase. The minimal FFT filtering is FFT1-FFF3 filtering, with the average correlation is 87%, but some acquisition still has the correlation less than 85%. The optimum is FFT1- FFT5 filtering, with the average correlation, is 92%, and all correlations are above 85% for all data during a year. Using the optimum scenario, the backscattered trend of paddy growth could be identified better and easier, so this technique is recommended for paddy growth monitoring.