Despeckling Based Data Augmentation Approach in Deep Learning Based Radar Target Classification

Speckle noise in SAR images distorts the image of the target and its surroundings, making difficult the target recognition task. Therefore, decomposition process of the speckle noise from the SAR images is important for radar automatic target recognition applications. Besides since the succes of the deep networks depends on the amount of data used in the training stage data augmentation increases classification rates. In this study, a new data augmentation approach based on despeckling has been proposed rather than the classical data augmentation techniques used in the processing of natural images in order to increase the deep learning-based radar target classification performance. Edge Avoiding Wavelet filter is used for speckle reduction task. Classification performances for original, despeckled and despeckling based data augmented datasets are compared on two traditional and basic CNN models. The experimental results show that despeckling based data augmentation method can improve the deep learning based radar automatic target recognition classification performance.