Comment on essd-2022-126

The surface signatures of meandering fronts and eddies have been regularly observed and documented in synthetic aperture radar (SAR) images. Wave‐current interactions, the suppression of short wind waves by natural film, and the varying wind field resulting from atmospheric boundary layer changes across an oceanic temperature front all contribute to the radar image manifestation of such mesoscale features. The corresponding imaging mechanisms are quantitatively explored using a new radar imaging model (Kudryavtsev et al., 2005) that solves the energy balance equation where wind forcing, viscous and wave breaking dissipation, wave‐wave interactions, and generation of short waves by breaking waves are taken into account. High‐quality and synoptic in situ observations of the surface conditions should ideally be used in this model. However, such data are rarely available. Instead, the fields of temperature and ocean current are herein derived from two distinct numerical ocean models. SAR image expressions of current fronts and eddies are then simulated based on these fields. The comparison of simulated images with European Remote Sensing (ERS) SAR and Envisat advanced SAR (ASAR) images is favorable. We consequently believe that the new radar imaging model provides promising capabilities for advancing the quantitative interpretation of current features manifested in SAR images.

Successful implementation of oil spill segmentation in synthetic aperture radar (SAR) images is vital for marine environmental protection. In this article, we develop an effective segmentation framework named DGNet, which performs oil spill segmentation by incorporating the intrinsic distribution of backscatter values in SAR images. Specifically, our proposed segmentation network is constructed with two deep neural modules running in an interactive manner, where one is the inference module to achieve latent feature variable inference from SAR images and the other is the generative module to produce oil spill segmentation maps by drawing the latent feature variables as inputs. Thus, to yield accurate segmentation, we take into account the intrinsic distribution of backscatter values in SAR images and embed it in our segmentation model. The intrinsic distribution originates from SAR imagery, describing the physical characteristics of oil spills. In the training process, the formulated intrinsic distribution guides efficient learning of optimal latent feature variable inference for oil spill segmentation. The efficient learning enables the training of our proposed DGNet with a small amount of image data. This is economically beneficial to oil spill segmentation where the availability of oil spill SAR image data is limited in practice. Additionally, benefiting from optimal latent feature variable inference, our proposed DGNet performs accurate oil spill segmentation. We evaluate the segmentation performance of our proposed DGNet with different metrics, and experimental evaluations demonstrate its effective segmentations.

Ocean oil spill detection from SAR images based on multi-channel deep learning semantic segmentation

Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing.

<p>The velocity of glaciers is commonly derived by offset tracking using pairwise cross correlation or feature matching of either optical or synthetic aperture radar (SAR) images.  SAR images, however, are inherently affected by noise-like radar speckle and require therefore much larger images patches for successful tracking compared to the patch size used with optical data. As a consequence, glacier velocity maps based on SAR offset tracking have a relatively low resolution compared to the nominal resolution of SAR sensors. Moreover, tracking may fail because small features on the glacier surface cannot be detected due to radar speckle. Although radar speckle can be reduced by applying spatial low-pass filters (e.g. 5x5 boxcar), the spatial smoothing reduces the image resolution roughly by an order of magnitude which strongly reduces the tracking precision. Furthermore, it blurs out small features on the glacier surface, and therefore tracking can also fail unless clear features like large crevasses are visible.</p><p>In order to create high resolution velocity maps from SAR images and to generate speckle-free radar images of glaciers, we present a new method that derives the glacier surface velocity field by correlating temporally averaged sub-stacks of a series of SAR images. The key feature of the method is to warp every pixel in each SAR image according to its temporally increasing offset with respect to a reference date. The offset is determined by the glacier velocity which is obtained by maximizing the cross-correlation between the averages of two sub-stacks. Currently, we need to assume that the surface velocity is constant during the acquisition period of the image series but this assumption can be relaxed to a certain extend.</p><p>As the method combines the information of multiple images, radar speckle are highly suppressed by temporal multi-looking, therefore the signal-to-noise ratio of the cross-correlation is significantly improved. We found that the method outperforms the pair-wise cross-correlation method for velocity estimation in terms of both the coverage and the resolution of the velocity field. At the same time, very high resolution radar images are obtained and reveal features that are otherwise hidden in radar speckle.</p><p>As the reference date, to which the sub-stacks are averaged, can be arbitrarily chosen a smooth flow animation of the glacier surface can be generated based on a limited number of SAR images. The presented method could build a basis for a new generation of tracking methods as the method is excellently suited to exploit the large number of emerging free and globally available high resolution SAR image time series.</p>

A probabilistic method has been developed that distinguishes oil spills from other similar sea surface features in synthetic aperture radar (SAR) images. It considers both the radiometric and the geometric characteristics of the areas being tested. In order to minimize the operator intervention, it adopts automatic selection criteria to extract the potentially polluted areas from the images. The method has an a priori percentage of correct classification higher than 90% on the training dataset; the performance is confirmed on a different dataset of verified slicks. Some analyses have been conducted using images with different radiometric and geometric resolutions to test its suitability with SAR images different from European Remote Sensing (ERS) satellite ones. The system and its ability to detect and classify oil and non‐oil surface features are described. Starting from a set of verified oil spills detected offshore and over the coastline, the ability of SAR to reveal oil spills is tested by analysing wind intensity, deduced from the image itself, and the distance from the coast.

The usage of a hypersonic platform for remote sensing application has promising prospects, especially for hypersonic platform-borne synthetic aperture radar (SAR) imaging. However, the high-speed of hypersonic platform will lead to extreme friction between the platform and air, which will cause the ionization of air. The ionized gas forms the plasma sheath wrapped around the hypersonic platform. The plasma sheath will severely affect the propagation of SAR signal and further affect the SAR imaging. Therefore, hypersonic platform-borne SAR imaging should be studied from a physical perspective. In this paper, hypersonic platform-borne SAR imaging under plasma sheath is analyzed. The SAR signal propagation in plasma sheath is computed using scatter matrix method. The proposed SAR signal model is verified by using a ground experiment system. Moreover, the effect of attenuation caused by plasma sheath on SAR imaging is studied under different SAR parameters and plasma sheath. The result shows that attenuation caused by plasma sheath will degrade the SAR imaging quality and even cause the point and area targets to be submerged into the noise. The real SAR images under plasma sheath also illustrate this phenomenon. Furthermore, by studying imaging results under different SAR and plasma parameters, it can be concluded that the severe degradation of SAR imaging quality appears at condition of high plasma sheath electron density and low SAR carrier frequency. The work in this paper will be beneficial for the study of hypersonic platform-borne SAR imaging and design of hypersonic SAR imaging systems in the future.

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.

Recognition of occluded objects in synthetic aperture radar (SAR) images is a significant problem for automatic target recognition. In this paper, we present a hidden Markov modeling (HMM) based approach for recognizing objects in synthetic aperture radar (SAR) images. We identify the peculiar characteristics of SAR sensors and using these characteristics we develop feature based multiple models for a given SAR image of an object. The models exploiting the relative geometry of feature locations or the amplitude of SAR radar return are based on sequentialization of scattering centers extracted from SAR images. In order to improve performance we integrate these models synergistically using their probabalistic estimates for recognition of a particular target at a specific azimuth. Experimental results are presented using both synthetic and real SAR images.

The multiscale normalized cut technique is used to detect oil spill in synthetic aperture radar (SAR) images. The proposed approach is a region-based one, which is able to capture and utilize spatial information in the image. Actual ENVISAT ASAR image was collected and used to validate the effectiveness and efficiency of the proposed approach for oil spill detection in SAR images.

Forecasting and monitoring polar lows are, to a large degree, based on satellite observations from passive radiometers and from scatterometer winds in addition to synoptic observations and numerical models. Synthetic aperture radar (SAR) brings higher resolution compared to other remotely sensed sources of ocean wind, such as scatterometer data and passive microwave wind products. The added information in polar low situations from SAR and the increased-resolution scatterometer wind fields are investigated. Statistically, higher variability in the MetOp Advanced Scatterometer (ASCAT) wind is clearly found during polar low situations compared to all situations. Slightly more variability is also seen in the ASCAT 12.5-km wind product compared to the operational European Centre for Medium-Range Weather Forecasts (ECMWF) model surface winds. In two analyzed polar low cases, Environmental Satellite (Envisat) Advanced SAR (ASAR) images reveal numerous interesting features, such as the sharp fronts and the location and strength of the strongest wind field in the polar low. It is likely that if SAR images are available to operational weather forecasting, that it can in some cases lead to earlier detection of polar lows. However, a reliable wind field from SAR is still needed.

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

This paper proposes a novel method on Synthetic Aperture Radar (SAR) Image denoising, which is based on context modeling combined with multiscale orthogonal bandlet coefficients. For the main influence of SAR image is multiplicative speckle noise, the logarithm uniform transform is used to convert it to additive noise. In this paper, the multiscale orthogonal bandlet transform is applied to noisy SAR Image. The contextual model is used to compute contextual values of bandlet coefficients on each scale. Therefore, bandlet coefficients are reconstructed into some parts by contextual values. The spatially adaptive soft-threshold (T) is to denoise SAR image in each part. Experimental results demonstrate that this method outperforms the methods of Ehance-Lee (E-lee), contextual model based on wavelet domain (Wavelet-context), and Hidden Markov Tree model bases on wavelet domain (Wavelet-hmt) on there indexes: Floating index (FI), Standard deviation and Edge saving index of vertical direction, by using a real SAR image. However, the method can not only reduce the speckle noise but also preserve edges and high frequency information, and gets considerable visual effect on SAR Image denoising.

A novel three-step method to reduce speckle in Synthetic Aperture Radar (SAR) images is presented. First, an edge detector is used to detect the edge orientations in SAR images. Second, using a data processing method named empirical mode decomposition, the SAR image is smoothed along four directions: horizontal, vertical, left diagonal and right diagonal, respectively. Third, the SAR image is reconstructed such that if the edge direction is horizontal, the pixel smoothed along the horizontal direction is used to reconstruct the image, and so on; if an edge is not detected, the mean of the four directionally smoothed images is used to reconstruct the image. Application of this method to SAR images has shown that it is well balanced in the quality of visual appearance, mean preservation, edge preservation, and reduction of the standard deviation.

ABSTRACTThe speckle noise of synthetic aperture radar (SAR) images limits its application in change detection. Compared with improved ratio (IR) and log-ratio (LR) operators, the neighborhood-based ratio (NR) technique can restrain the influence of speckle noise and is more suitable for change detection in SAR images. However, we find three drawbacks of NR by analyzing this method carefully. To overcome these defects, we propose an improved neighborhood-based ratio (INR) approach for change detection in SAR images. INR restructures the NR operator to exploit the neighborhood information more reasonably and is expected to reduce the impact of speckle noise better. IR, LR, mean ratio operator, NR, and INR are tested on two data sets to compare their performances in change detection of SAR images. Experimental results show that the proposed method can obtain better difference image than other state-of-art methods and improve the accuracy of change detection in SAR images effectively.