Synthetic Aperture Radar Intensity Research Articles

Synthetic aperture radar (SAR) detects large gas seeps in Alaska lakes

Reservoirs of 14C-depleted methane (CH4), a potent greenhouse gas, residing beneath permafrost are vulnerable to escape where permafrost thaw creates open-talik conduits. However, little is known about the magnitude and variability of this methane source or its response to climate change. Remote-sensing detection of large gas seeps would be useful for establishing a baseline understanding of sub-permafrost methane seepage, as well as for monitoring these seeps over time. Here we explored synthetic aperture radar’s (SAR) response to large sub-permafrost gas seeps in an interior Alaskan lake. In SAR scenes from 1992 to 2011, we observed high perennial SAR L-band backscatter (σ 0) from a ∼90 m-wide feature in the winter ice of interior Alaska’s North Blair Lake (NBL). Spring and fall optical imagery showed holes in the ice at the same location as the SAR anomaly. Through field work we (1) confirmed gas bubbling at this location from a large pockmark in the lakebed, (2) measured flux at the location of densest bubbles (1713 ± 290 mg CH4 m−2 d−1), and (3) determined the bubbles’ methane mixing ratio (6.6%), radiocarbon age (18 470 ± 50 years BP), and δ13CCH4 values (−44.5 ± 0.1‰), which together may represent a mixture of sources and processes. We performed a first order comparison of SAR σ 0 from the NBL seep and other known sub-permafrost methane seeps with diverse ice/water interface shapes in order to evaluate the variability of SAR signals from a variety of seep types. Results from single-polarized intensity and polarimetric L-band SAR decompositions as well as dual-polarized C-band SAR are presented with the aim to find the optimal SAR imaging parameters to detect large methane seeps in frozen lakes. Our study indicates the potential for SAR remote sensing to be used to detect and monitor large, sub-permafrost gas seeps in Arctic and sub-Arctic lakes.

A Generalized Beta Prime Distribution as the Ratio Probability Density Function for Change Detection Between Two SAR Intensity Images With Different Number of Looks

A Generalized Beta Prime Distribution as the Ratio Probability Density Function for Change Detection Between Two SAR Intensity Images With Different Number of Looks

Automatic Monitoring of Oil Tank 3D Geometry and Storage Changes With Interferometric Coherence and SAR Intensity Information

Automatic Monitoring of Oil Tank 3D Geometry and Storage Changes With Interferometric Coherence and SAR Intensity Information

Forest/Nonforest Segmentation Using Sentinel-1 and -2 Data Fusion in the Bajo Cauca Subregion in Colombia

Remote sensing technologies have been successfully used for deforestation monitoring, and with the wide availability of satellite products from different platforms, forest monitoring applications have grown in recent years. The observed potential in these technologies motivates the development of forest mapping and monitoring tools that could also be used for neighboring applications like agriculture or land-use mapping. A literature review confirmed the research areas of interest in deforestation monitoring using synthetic aperture radar (SAR) and data fusion techniques, which guided the formulation of the method developed in this article consisting of a data preprocessing workflow for SAR (Sentinel-1) and multispectral (Sentinel-2) data and a procedure for the selection of a machine learning model for forest/nonforest segmentation evaluated in different combinations of Sentinel-1 and Sentinel-2 bands. The selected model is a random forest algorithm that uses C-band SAR dual-polarimetric bands, intensity features, and vegetation indices derived from optical/multispectral data. The selected random forest classifier’s balanced accuracies were 79–81%, and the f1-scores were 0.72–0.76 for the validation set. The results allow the obtention of yearly forest/nonforest and forest loss maps in the study area of Bajo Cauca in Colombia, a region with a documented high deforestation rate.

Exploring Sentinel-1 backscatter time series over the Atacama Desert (Chile) for seasonal dynamics of surface soil moisture

Recent research indicates an inverse relation between Synthetic Aperture Radar (SAR) signal and near-surface soil moisture (SM) over very dry sediments, arid to hyper-arid soils resp., caused by subsurface scattering effects. This phenomenon can lead to large errors when it comes to modelling and remote sensing-based estimation of SM. While the effect of subsurface scattering and its influence on SM estimates is well described and modelled in literature, its actual presence in recorded SAR data is largely unknown. Here we investigate the relation between C-Band SAR backscatter and SM in the hyper-arid environment of the Atacama Desert (Chile). Time series (2018–2020) of Sentinel-1 VV/VH intensities are compared to in situ SM, measured at 17 stations located across the Atacama Desert. Linear and non-linear regression modelling is applied to uncover the relationship between the SAR intensities and in situ SM, while, in addition, SM variations triggered by seasonal varying humidity (i.e., not by precipitation) are investigated. Results indicate (i) a very weak linear relationship between SM variations and SAR intensities (VV/VH) for most meteorological stations in the Atacama (R2 < 0.5). In particular, noticeable and significant exceptions are found for stations located in the northern uplands of the Coastal Cordillera and on sediments, which are characterized by thick atmospheric dust deposits on top of subsurface cemented crusts; (ii) over these sites a strong inverse linear relationship is present (R2 up to 0.84) and the observable seasonal variations in SAR intensity are linked to the seasonal variations in SM; (iii) remarkably low changes in SM (ranging from approx. 1 to 3%) lead to comparable high changes in SAR intensity (up to 5.5 dB in VH and up to 4.0 dB in VV); (iv) employing a non-linear regression modelling to the SAR time series allowed identifying the occurrence and the strength of seasonal variations over the central desert. This study contributes to a better understanding of SAR scattering over arid to hyper-arid soils, indicates a moisture-regulated complex interplay of surface and subsurface scattering, and proves the presence of subsurface scattering effects in Sentinel-1C-Band data to occur over large parts of the Atacama Desert.

Glacier Motion Monitoring Using a Novel Deep Matching Network with SAR Intensity Images

Synthetic Aperture Radar technology is highly convenient for monitoring the glacier surface motion in unfavorable areas due to its advantages of being independent of time and weather conditions. A novel glacier motion monitoring method based on the deep matching network (DMN) is proposed in this paper. The network learns the relationship between the glacier SAR image patch-pairs and the corresponding matching labels in an end-to-end manner. Unlike conventional methods that utilize shallow feature tracking, the DMN performs a similarity measurement of deep features, which comprises feature extraction and a metric network. Feature extraction adopts the framework of a Siamese neural network to improve the training efficiency and dense connection blocks to increase the feature utilization. In addition, a self-sample learning method is introduced to generate training samples with matching labels. The experiments are performed on simulated SAR images and real SAR intensity images of the Taku Glacier and the Yanong Glacier, respectively. The results confirm the superiority of the DMN presented in the paper over other methods, even in case of strong noise. Furthermore, a quantitative 2D velocity field of real glaciers is obtained to provide reliable support for high-precision, long-term and large-scale automatic glacier motion monitoring.

K-Bessel regression model for speckled data

Synthetic aperture radar (SAR) provides an efficient way to monitor the Earth's surface. But the speckle noise that the SAR system generates when acquiring images makes it difficult to understand and interpret SAR intensity features. To automatically analyze SAR images, this paper presents a K-Bessel regression (KBR) model in which a function of the mean intensity response is explained by other features (or covariates) determined in parallel. Some mathematical properties of this regression are derived and discussed in the context of the physical origin of the SAR image. A maximum likelihood estimation procedure is planned and its performance is quantified by Monte Carlo experiments. An application to real data obtained from a polarimetric SAR image of San Francisco Bay is realized. Results show that both the KBR-based processing is more informative than the unconditional approach to describe SAR intensity and that our proposal can outperform the normal and gamma regression models. Finally, it is shown that the KBR model is useful to reproduce the relief signal of one channel from the intensity values of the other.

Entropy Estimators in SAR Image Classification.

Remotely sensed data are essential for understanding environmental dynamics, for their forecasting, and for early detection of disasters. Microwave remote sensing sensors complement the information provided by observations in the optical spectrum, with the advantage of being less sensitive to adverse atmospherical conditions and of carrying their own source of illumination. On the one hand, new generations and constellations of Synthetic Aperture Radar (SAR) sensors provide images with high spatial and temporal resolution and excellent coverage. On the other hand, SAR images suffer from speckle noise and need specific models and information extraction techniques. In this sense, the family of distributions is a suitable model for SAR intensity data because it describes well areas with different degrees of texture. Information theory has gained a place in signal and image processing for parameter estimation and feature extraction. Entropy stands out as one of the most expressive features in this realm. We evaluate the performance of several parametric and non-parametric Shannon entropy estimators as input for supervised and unsupervised classification algorithms. We also propose a methodology for fine-tuning non-parametric entropy estimators. Finally, we apply these techniques to actual data.

Applying Machine Learning and Time-Series Analysis on Sentinel-1A SAR/InSAR for Characterizing Arctic Tundra Hydro-Ecological Conditions

Synthetic aperture radar (SAR) is a widely used tool for Earth observation activities. It is particularly effective during times of persistent cloud cover, low light conditions, or where in situ measurements are challenging. The intensity measured by a polarimetric SAR has proven effective for characterizing Arctic tundra landscapes due to the unique backscattering signatures associated with different cover types. However, recently, there has been increased interest in exploiting novel interferometric SAR (InSAR) techniques that rely on both the amplitude and absolute phase of a pair of acquisitions to produce coherence measurements, although the simultaneous use of both intensity and interferometric coherence in Arctic tundra image classification has not been widely tested. In this study, a time series of dual-polarimetric (VV, VH) Sentinel-1 SAR/InSAR data collected over one growing season, in addition to a digital elevation model (DEM), was used to characterize an Arctic tundra study site spanning a hydrologically dynamic coastal delta, open tundra, and high topographic relief from mountainous terrain. SAR intensity and coherence patterns based on repeat-pass interferometry were analyzed in terms of ecological structure (i.e., graminoid, or woody) and hydrology (i.e., wet, or dry) using machine learning methods. Six hydro-ecological cover types were delineated using time-series statistical descriptors (i.e., mean, standard deviation, etc.) as model inputs. Model evaluations indicated SAR intensity to have better predictive power than coherence, especially for wet landcover classes due to temporal decorrelation. However, accuracies improved when both intensity and coherence were used, highlighting the complementarity of these two measures. Combining time-series SAR/InSAR data with terrain derivatives resulted in the highest per-class F1 score values, ranging from 0.682 to 0.955. The developed methodology is independent of atmospheric conditions (i.e., cloud cover or sunlight) as it does not rely on optical information, and thus can be regularly updated over forthcoming seasons or annually to support ecosystem monitoring.

Compound truncated Poisson gamma distribution for understanding multimodal SAR intensities

In recent years, many works have addressed the proposal of new probability models by theoretic and applied reasons. Specifically, mixture models have been indicated to describe phenomena whose resulting data impose high flexibility. One drawback of these tools is the high number of parameters involved, which implies hard inference procedures. To outperform this gap, we propose a new model that is able to describe multimodal behaviors with only three parameters, called compound truncated Poisson gamma (CTrPGa) distribution. Some properties of the CTrPGa law are derived and discussed: hazard, characteristic and cumulative functions and ordinary moments. Beyond, moment estimation, maximum likelihood estimation (via the expectation maximization algorithm) and empirical characteristic function methods for CTrPGa parameters are furnished. The first of them may be reduced to solve one nonlinear equation, which facilitates its use. We perform a simulation analysis to compare the performance of the three estimation methods studied. Moreover, since the gamma distribution and its mixture versions are commonly used to characterize synthetic aperture radar (SAR) intensities, we perform some real experiments with SAR imagery. The results present evidence that our model is a reasonable assumption that can be taken into account in the pre-processing step of such images.

Oil Spill Detection Based on Deep Convolutional Neural Networks Using Polarimetric Scattering Information From Sentinel-1 SAR Images

Oil spill accidents can cause severe ecological disasters; hence, the timely and effective detection of oil spills on the marine surface is of great significance. Synthetic aperture radar (SAR) is very suitable for large-scale oil spill monitoring. As a more advanced form of SAR, polarimetric SAR (PolSAR) can provide more scattering information of land objects, which can help to improve the accuracy of oil spill detection. However, the current studies of oil spill detection by SAR data have mainly focused on using SAR intensity or amplitude information, and the phase information and other polarimetric information have not been fully utilized. To solve this problem, using Sentinel-1 dual-polarimetric images as the data source, this article presents an intelligent oil spill detection architecture based on a deep convolutional neural network (DCNN), in which both the amplitude information and phase information are utilized. Furthermore, to improve the feature discrimination capability, the Cloude polarimetric decomposition parameters are also integrated into the proposed model. The results show that the improved DeepLabv3+ model, which takes ResNet-101 as the backbone network and group normalization (GN) as the normalization layer, can achieve superior performance than those traditional methods. Moreover, the model is better able to capture the fine details of oil spill instances and can achieve fine-scale segmentation.

On the Use of Ocean Surface Doppler Velocity for Oceanic Front Extraction From Chinese Gaofen-3 SAR Data

Oceanic front extraction from synthetic aperture radar (SAR) images is of great significance to the study of marine ecosystems. At present, the methods for oceanic front extraction are usually based on SAR intensity images, which treat oceanic fronts as edge-like features in the SAR images. However, affected by radar parameters and sea state, sometimes, oceanic front signatures may not be clearly visible in the SAR intensity images. Therefore, existing methods are limited. In order to solve this problem, a method combining intensity and Doppler information for oceanic front extraction is proposed. Using Chinese Gaofen-3 single-look complex (SLC) data, three cases where oceanic front signatures are clearly visible, partially visible, and extremely weak in the SAR intensity images are investigated, which demonstrate how the Doppler velocity gradient across a front can be leveraged to enhance the extraction of oceanic fronts from SAR data. The results show that the Doppler data, as supplementary information, not only can complement the oceanic fronts extracted from SAR intensity images, but also can be used as a reference for the oceanic fronts extracted from SAR intensity images.

Deep-learning-based single-image height reconstruction from very-high-resolution SAR intensity data

Originally developed in fields such as robotics and autonomous driving with image-based navigation in mind, deep learning-based single-image depth estimation (SIDE) has found great interest in the wider image analysis community. Remote sensing is no exception, as the possibility to estimate height maps from single aerial or satellite imagery bears great potential in the context of topographic reconstruction. A few pioneering investigations have demonstrated the general feasibility of single image height prediction from optical remote sensing images and motivate further studies in that direction. With this paper, we present the first-ever demonstration of deep learning-based single image height prediction for the other important sensor modality in remote sensing: synthetic aperture radar (SAR) data. Besides the adaptation of a convolutional neural network (CNN) architecture for SAR intensity images, we present a workflow for the generation of training data, and extensive experimental results for different SAR imaging modes and test sites. Since we put a particular emphasis on transferability, we are able to confirm that deep learning-based single-image height estimation is not only possible, but also transfers quite well to unseen data, even if acquired by different imaging modes and imaging parameters.

Country‐wide flood exposure analysis using Sentinel‐1 synthetic aperture radar data: Case study of 2019 Iran flood

AbstractExtreme precipitation and flooding often lead to human and economic losses. However, the high‐resolution nationwide flooding exposure data are scarce. Availability of all‐weather space‐borne SAR satellite data can potentially improve the ability to generate high‐resolution flood map extent and exposure globally. In Iran, flooding is a major concern, given the socioeconomic vulnerabilities and increased likelihood of climate extremes. Iran experienced extreme flooding during January to March 2019, attributed to significant precipitation during October 2018 to March 2019, which is well above the long‐term averages for 1999–2019. Using Pettitt and Mann Kendall tests, nationwide precipitation records were identified by significant decreasing and increasing trends in north and south, respectively. Utilizing 673 Sentinel‐1 SAR intensity images, we applied a fast‐marching algorithm for image segmentation in combination with a Bayesian framework to obtain high‐resolution probabilistic flood maps. We found, 22, 9, and 15 states in January, February, and March, respectively, experienced flooding that covered &gt;15% of their area with high flooded area percent in the northwestern and southeastern region. We estimated that &gt;15, &gt;11.32, and &gt;11.33 million people were exposed to floods in January, February, and March, respectively. Our datasets inform flooding models and management efforts under increasing climate extremes and changing land use and cover.

A Multiscale Joint Deep Neural Network for Glacier Contour Extraction

Rapid and accurate acquisition of glacier regional changes is of great significance to the study of glaciers. Among all satellite images, Synthetic Aperture Radar (SAR) data has a great advantage in monitoring the glaciers in harsh weather conditions. Conventionally, glacier boundaries are manually delineated on images. However, this is a time-consuming process, especially in the batch process of large-area data. In this paper, we propose a Multiscale Joint Deep Neural Network (MJ-DNN) for large-scale glaciers contour extraction using single-polarimetric SAR intensity images. Based on U-Net, the proposed method has been improved in three aspects. First, Atrous Separable Convolution is used instead of convolution with the down-sampling part. Second, we propose a multiscale joint convolution layer to obtain information at multiple scales. Third, we deepen the network with the residual connection structure for higher-level features. At the final layer, we optimize the network result with the conditional random field method. To validate our approach, we test it on three glaciers and we compare the segmentation results of four different methods in parallel. The results show that the intersection over the union of the proposed method is the most efficient.

Detection of Collapsed Bridges from Multi-Temporal SAR Intensity Images by Machine Learning Techniques

Bridges are an important part of road networks in an emergency period, as well as in ordinary times. Bridge collapses have occurred as a result of many recent disasters. Synthetic aperture radar (SAR), which can acquire images under any weather or sunlight conditions, has been shown to be effective in assessing the damage situation of structures in the emergency response phase. We investigate the backscattering characteristics of washed-away or collapsed bridges from the multi-temporal high-resolution SAR intensity imagery introduced in our previous studies. In this study, we address the challenge of building a model to identify collapsed bridges using five change features obtained from multi-temporal SAR intensity images. Forty-four bridges affected by the 2011 Tohoku-oki earthquake, in Japan, and forty-four bridges affected by the 2020 July floods, also in Japan, including a total of 21 collapsed bridges, were divided into training, test, and validation sets. Twelve models were trained, using different numbers of features as input in random forest and logistic regression methods. Comparing the accuracies of the validation sets, the random forest model trained with the two mixed events using all the features showed the highest capability to extract collapsed bridges. After improvement by introducing an oversampling technique, the F-score for collapsed bridges was 0.87 and the kappa coefficient was 0.82, showing highly accurate agreement.

Deriving exclusion maps from C-band SAR time-series in support of floodwater mapping

Synthetic Aperture Radar (SAR) intensity is used as an input to many flood-mapping algorithms. The appearance of floodwater tends to cause a substantial decrease of backscatter intensity over scarcely vegetated terrain. However, limitations exist in areas where the SAR backscatter is not sufficiently sensitive to surface changes, e.g. shadow areas due to topography or obstacles on the ground, densely forested areas, sand, etc. Thus, we argue that it is of paramount importance to complement any SAR-based flood extent map with an exclusion map (EX-map) indicating all areas where the presence of water cannot be derived from SAR intensity observations. In this study, we introduce a methodology for generating an EX-map based on the analysis of time-series of SAR backscatter data. In particular, the identification of the EX-map is based on the combined use of three temporal indicators based on backscatter statistics, i.e. temporal median, minimum and standard deviation. As a test case, EX-maps were derived from Sentinel-1 data acquired during the 2014–2019 time period from six representative study sites. Reference maps were generated using a global land cover map, Digital Elevation Model (DEM)-derived shadow/layover masks, global urban footprint (GUF) data and a Sand Exclusion Layer (SEL). The cross-comparison revealed that the EX-map was consistent with reference maps obtained from other data sources.

Lognormal Random Field models to identify temporal Land cover changes using full polarimetric L-Band SAR imagery

Multiplicative Autoregressive Random Field (MAR) based texture models have been identified as one of the most appropriate models for SAR intensity images to capture the stochastic spatial interaction among neighboring pixels. But very few studies have tested their viability particularly in disaster applications. In this paper, we analyse the MAR texture models for their advantageous in land cover change detection compared to the changes resulting from logarithm of SAR image intensity and speckle filtered SAR imagery. The paper shows that lognormal random fields with multiplicative spatial interactions in the form of MAR models can be an effective alternative to suppress speckle noise and model SAR image intensity in time series data analysis. The pre and post disaster observational data of the Tohoku earthquake, in the east coast of Japan, acquired by the Advanced Land Observation Satellite (ALOS)/phased array type L-band synthetic aperture radar (PALSAR) were synthesized using MAR model based texture measures. Two of the main texture descriptors of the MAR model were considered primarily in this study. Those are the neighborhood weighting and the noise variance parameters. A 2nd order neighborhood configuration was used to estimate them. We present a variogram based analysis, structural similarity index measure (SSIM), and the mean ratio detector (MRD) as three different approaches to analyse the changes in land cover using radar texture. The change detection results of the MRD were further tested using area error proportion (AEP), root mean square error (RMSE) and correlation coefficient (CC), keeping normalized ratio, principle component analysis (PCA) and adaptive Lee filtered polarimetric intensity based change as the references.

Processing Framework for Landslide Detection Based on Synthetic Aperture Radar (SAR) Intensity-Image Analysis

We present an object-based image analysis (OBIA) approach to identify temporal changes in radar-intensity images and to locate land-cover changes caused by mass-wasting processes at small to large scales, such as landslides. Our approach is based upon change detection in SAR intensity images that remain in their original imaging coordinate system rather than being georeferenced and map-projected, in order to reduce accumulation of filtering artifacts and other unwanted effects that would deteriorate the detection efficiency. Intensity images in their native slant-range coordinate frame allow for a consistent level of detection of land-cover changes. By analyzing intensity images, a much faster response can be achieved and images can be processed as soon as they are made publicly available. In this study, OBIA was introduced to systematically and semiautomatically detect landslides in image pairs with an overall accuracy of at least 60% when compared to in-situ landslide inventory data. In this process, the OBIA feature extraction component was supported by derived data from a polarimetric decomposition as well as by texture indices derived from the original image data. The results shown here indicate that most of the landslide events could be detected when compared to a closer visual inspection and to established inventories, and that the method could therefore be considered as a robust detection tool. Significant deviations are caused by the limited geometric resolution when compared to field data and by an additional detection of stream-related sediment redeposition in our approach. This overdetection, however, turns out to be potentially beneficial for assessing the risk situation after landslide events.

Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images

Typhoon Hagibis passed through Japan on October 12, 2019, bringing heavy rainfall over half of Japan. Twelve banks of seven state-managed rivers collapsed, flooding a wide area. Quick and accurate damage proximity maps are helpful for emergency responses and relief activities after such disasters. In this study, we propose a quick analysis procedure to estimate inundations due to Typhoon Hagibis using multi-temporal Sentinel-1 SAR intensity images. The study area was Ibaraki Prefecture, Japan, including two flooded state-managed rivers, Naka and Kuji. First, the completely flooded areas were detected by two traditional methods, the change detection and the thresholding methods. By comparing the results in a part of the affected area with our field survey, the change detection was adopted due to its higher recall accuracy. Then, a new index combining the average value and the standard deviation of the differences was proposed for extracting partially flooded built-up areas. Finally, inundation maps were created by merging the completely and partially flooded areas. The final inundation map was evaluated via comparison with the flooding boundary produced by the Geospatial Information Authority (GSI) and the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) of Japan. As a result, 74% of the inundated areas were able to be identified successfully using the proposed quick procedure.