A deep neural network learning-based speckle noise removal technique for enhancing the quality of synthetic-aperture radar images

The speckle noise present in synthetic-aperture radar (SAR) images is responsible for hindering the extraction of the exact information that needs to be utilized for potential remote sensing applications. Thus the quality of SAR images needs to be enhanced by removing speckle noise in an effective manner. In this paper, A Deep Neural Network-based Speckle Noise Removal Technique (DNN-SNRT) is proposed that utilizes the benefits of convolution and Long Short Term Memory-based neural networks to enhance the quality of SAR images. The proposed DNN-SNRT uses multiple radar intensity images that are archived from the specific area of interest to facilitate the self-learning of the intensity features derived from the image patches. The proposed DNN-SNRT incorporates a dual neural network to remove speckle noise and flexibly estimates the thresholds and weights to achieve an effective SAR image quality improvement. The proposed DNN-SNRT is capable of automatically updating the intensity features of SAR images during the training process. Experimental investigation of the proposed DNN-SNRT conducted based on TerraSAR-X images confirmed the superior enhancement of image quality over comparable recent filters. The results of the DNN-SNRT scheme were also proved that it is able to reduce noise and preserve edges during the image quality enhancement process.

Automated summary

This paper presents a Deep Neural Network-based Speckle Noise Removal Technique, using self-learning to extract intensity features for enhancing SAR image quality, capable of automatically updating intensity features, removing noise, and preserving edges during the training process.