Improved Noise Filtering Technique For Wake Detection In SAR Image Under Rough Sea Condition

Abstract

Sea surface is rough when the weather condition at sea is rough due to strong wind, waves, swell and storms. Under the rough sea condition, the propagation of radar energy and the subsequent radar coverage is strongly influenced by various atmospheric effects, such as, strong wind, wave height, weather condition, oceanic currents and rainstorms. The identification of ship wakes in Synthetic Aperture Radar (SAR) image under the rough sea condition is viewed as a highly complex task for the real time monitoring and surveillance applications. It becomes a quite big challenge due to coherent radiation of backscattering signals and the multiplicative speckle noise found in SAR images. The objective of this work is to develop an optimized Discrete Wavelet Transform (DWT) based on Synergistic Fibroblast Optimization (SFO) algorithm for filtering speckle noise in SAR image which are captured under rough sea condition. An improved filtering technique is tested with the real time SAR images acquired from European Space Agency (ESA) sentinel scientific data hub and its efficacy is further validated by employing Discrete Radon Transform (DRT) method to detect ship wakes (linear signature) in SAR image under rough sea surface. The performance of SFO based wavelet transform is evaluated and compared with conventional DWT families, namely, daubechies, coiflet, symlet, discrete meyer, biorthogonal and reverse biorthogonal to conduct the better investigation of this study. Investigation of results illustrates the effectiveness of optimized method, in terms of, noise suppression and its implication on radon transform method to localize the identification of ship wakes in SAR imagery.