Special Issue on Polarimetric SAR Techniques and Applications

López-Martínez, Carlos; López-Sánchez, Juan M.

doi:10.3390/app7080768

Cited by 2 publications

(5 citation statements)

References 9 publications

(6 reference statements)

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“…Image/noise modelling is based on general knowledge about image and noise properties in SAR images [2,6,7] as well as on information obtained by analysis of real-life Sentinel-1 images [31,45]. A first assumption is that a noise is pure multiplicative.…”

Section: Image/noise Model Filter and Quality Metricsmentioning

confidence: 99%

“…One might wonder that in this paper we concentrate on considering the DCT-based filter adapted to suppress pure multiplicative spatially correlated noise [23,31] while there are numerous filters proposed for this (see [6][7][8][9][10][11][12][13][14][15][16][17][18] and references therein). The reasons for analyzing the DCT-based filter are the following.…”

Section: Image/noise Model Filter and Quality Metricsmentioning

confidence: 99%

“…The main stages of DCT-based filtering are the following: perform direct 2D DCT in each block; apply thresholding to DCT-coefficients; perform inverse 2D DCT; aggregate filtering results for overlapping blocks [24,31]. Let us give some details and additional explanations for each stage.…”

Section: Image/noise Model Filter and Quality Metricsmentioning

confidence: 99%

“…This is especially important for data from new sensors such as Sentinel-1 [30] that provide a large number of RS images with high periodicity. Note that the dual-polarization radar data offered by Sentinel-1 have been successfully exploited in several important applications [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, one aspect of novelty of this paper is that prediction approach is designed for the specific type of RS images, namely, dual-polarization multilook Sentinel-1 images [30,31,45]. Another aspect is that we analyze different visual quality metrics that are shown to be predictable.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

2019

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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.

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Section: Image/noise Model Filter and Quality Metricsmentioning

confidence: 99%