Statistical Properties of an Unassisted Image Quality Index for SAR Imagery

Gómez, Luis; Ospina, Raydonal; Frery, Alejandro C.

doi:10.3390/rs11040385

Cited by 9 publications

(6 citation statements)

References 51 publications

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“…The proposed method seems very satisfactory also under this point of view. This is also confirmed numerically by the no-reference quality index M [55,56], which compares the statistical distribution of the ratio image with that of the theoretical speckle. The analysis was carried out on a set of homogeneous areas of the image, automatically selected by the method.…”

Section: Experiments On Simulated Imagessupporting

confidence: 57%

“…In the absence of a clean reference, we used visual inspection of despeckled and ratio images to assess the filters' properties, especially for what concerns preservation of image details. Instead, speckle suppression ability was measured objectively through the equivalent number of looks (ENL) computed on homogeneous regions of the image and by means of the no-reference image quality index M proposed in [55,56].…”

Section: Experimental Validationmentioning

confidence: 99%

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Nonlocal CNN SAR Image Despeckling

et al. 2020

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We propose a new method for SAR image despeckling, which performs nonlocal filtering with a deep learning engine. Nonlocal filtering has proven very effective for SAR despeckling. The key idea is to exploit image self-similarities to estimate the hidden signal. In its simplest form, pixel-wise nonlocal means, the target pixel is estimated through a weighted average of neighbors, with weights chosen on the basis of a patch-wise measure of similarity. Here, we keep the very same structure of plain nonlocal means, to ensure interpretability of results, but use a convolutional neural network to assign weights to estimators. Suitable nonlocal layers are used in the network to take into account information in a large analysis window. Experiments on both simulated and real-world SAR images show that the proposed method exhibits state-of-the-art performance. In addition, the comparison of weights generated by conventional and deep learning-based nonlocal means provides new insight into the potential and limits of nonlocal information for SAR despeckling.

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Section: Experiments On Simulated Imagessupporting

confidence: 57%

Section: Experimental Validationmentioning

confidence: 99%

Nonlocal CNN SAR Image Despeckling

et al. 2020

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“…Maps of pixel ratios, or better of logarithm of pixel ratios (LR), namely difference in backscatters that have been expressed in decibels (dB). are useful for detecting changes [26] with good preservation of geometric details [27], but limited capabilities of rejecting false alarms [12]. The main drawback of features relying on pixel ratios is that they can capture only changes generated by variations in first-order statistics.…”

Section: A Log-ratio Operatormentioning

confidence: 99%

Increasing the Detection Accuracy of Bi-Temporal Changes via Speckle Whitening of Single-Look Complex Synthetic Aperture Radar Images

Alparone,

Argenti,

Arienzo

et al. 2024

IEEE Access

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This study employs an unsupervised procedure to spatially decorrelate fully-developed speckle in single-look complex (SLC) synthetic aperture radar (SAR) images. The goal is evaluating the extent to which the spatial correlation of the noise induced by the SAR processor affects the detection accuracy of temporal variations of land-cover between two one-look images of the same landscape acquired on different dates. To simulate the scenario, we have spatially correlated a synthetic map of white complex circular symmetric Gaussian noise by using a two-dimensional separable Hamming window in the Fourier domain. The correlated complex speckle field has been modulated by a noise-free optical view, to simulate an SLC SAR image. Subsequently, we have reduced the correlation of the SLC image through a whitening process and calculated the modulus of the complex image. We have applied various methods of statistical change detection for real-valued SAR data, and compared the accuracy of change maps in the following cases: i) ideally uncorrelated noise, ii) correlated noise, iii) correlated noise that has been decorrelated. The study considers three change detection algorithms, ranging from the basic Log-Ratio operator preceded by despeckling to advanced parametric and nonparametric methods based on Kullback-Leibler distance and mean-shift clustering of bivariate scatterplots of local means. Simulation results demonstrate consistent performance improvements, in terms of both geometric accuracy and reduced number of false alarms.INDEX TERMS Inverse filtering, noise whitening, SAR image change detection, SAR processor, singlelook complex (SLC) images, synthetic aperture radar (SAR), tapering windows.

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“…Although many SAR filtering techniques are developed, most of them reduces noise at the price of damaging image details, which can also propagate to inundation mapping. Modern techniques such as the selection of the optimal filter [39] and machine learning may offer some advanced way in suppressing speckle.…”

Section: Error Sourcesmentioning

confidence: 99%

Inundation Extent Mapping by Synthetic Aperture Radar: A Review

et al. 2019

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Recent flood events have demonstrated a demand for satellite-based inundation mapping in near real-time (NRT). Simulating and forecasting flood extent is essential for risk mitigation. While numerical models are designed to provide such information, they usually lack reference at fine spatiotemporal resolution. Remote sensing techniques are expected to fill this void. Unlike optical sensors, synthetic aperture radar (SAR) provides valid measurements through cloud cover with high resolution and increasing sampling frequency from multiple missions. This study reviews theories and algorithms of flood inundation mapping using SAR data, together with a discussion of their strengths and limitations, focusing on the level of automation, robustness, and accuracy. We find that the automation and robustness of non-obstructed inundation mapping have been achieved in this era of big earth observation (EO) data with acceptable accuracy. They are not yet satisfactory, however, for the detection of beneath-vegetation flood mapping using L-band or multi-polarized (dual or fully) SAR data or for urban flood detection using fine-resolution SAR and ancillary building and topographic data.

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Statistical Properties of an Unassisted Image Quality Index for SAR Imagery

Cited by 9 publications

References 51 publications

Nonlocal CNN SAR Image Despeckling

Nonlocal CNN SAR Image Despeckling

Increasing the Detection Accuracy of Bi-Temporal Changes via Speckle Whitening of Single-Look Complex Synthetic Aperture Radar Images

Inundation Extent Mapping by Synthetic Aperture Radar: A Review

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