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NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency

1
Department of Information and Communication Technologies, National Aerospace University, 61070 Kharkiv, Ukraine
2
Computational Imaging Group, Tampere University, 33720 Tampere, Finland
*
Author to whom correspondence should be addressed.
Geosciences 2019, 9(7), 290; https://doi.org/10.3390/geosciences9070290
Received: 17 May 2019 / Revised: 23 June 2019 / Accepted: 27 June 2019 / Published: 29 June 2019
(This article belongs to the Special Issue Image processing and satellite imagery analysis in environments)
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Abstract

Images acquired by synthetic aperture radars are degraded by speckle that prevents efficient extraction of useful information from radar remote sensing data. Filtering or despeckling is a tool often used to improve image quality. However, depending upon image and noise properties, the quality of improvement can vary. Besides, a quality can be characterized by different criteria or metrics, where visual quality metrics can be of value. For the case study of discrete cosine transform (DCT)based filtering, we show that improvement of radar image quality due to denoising can be predicted in a simple and fast way, especially if one deals with particular type of radar data such as images acquired by Sentinel-1. Our approach is based on application of a trained neural network that, in general, might have a different number of inputs (features). We propose a set of features describing image and noise statistics from different viewpoints. From this set, that contains 28 features, we analyze different subsets and show that a subset of the 13 most important and informative features leads to a very accurate prediction. Test image generation and network training peculiarities are discussed. The trained neural network is then tested using different verification strategies. The results of the network application to test and real-life radar images are presented, demonstrating good performance for a wide set of quality metrics. View Full-Text
Keywords: remote sensing; speckle; Sentinel-1; filtering; neural network; multilayer perceptron; prediction of denoising efficiency remote sensing; speckle; Sentinel-1; filtering; neural network; multilayer perceptron; prediction of denoising efficiency
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rubel, O.; Lukin, V.; Rubel, A.; Egiazarian, K. NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency. Geosciences 2019, 9, 290.

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