Detecting rice transplantation dates is crucial for understanding its effect on grain yield and water consumption at regional scales. Traditionally, identifying the rice transplantation phase using dual-polarized (dual-pol) synthetic aperture radar (SAR) data has relied on backscatter intensity due to its characteristic low values during the flooding stage. This study leverages a recently proposed dual-pol radar surface index (DpRSI) to analyze the spatiotemporal dynamics of the rice transplantation phases. Using this index, we propose an unsupervised framework to identify rice transplantation dates. The framework is evaluated using ground-truth (GT) data over rice-cultivated regions in Vijayawada, India, during the kharif season 2018, demonstrating its effectiveness in detecting shifts in transplantation dates over a large spatial extent.

Accurate flight trajectory knowledge is crucial for airborne and drone-borne repeat-pass Synthetic Aperture Radar (SAR) interferometry. Given that Global Navigation Satellite System (GNSS) inaccuracies are comparable to typical radar wavelengths, especially at high-frequency bands (X-band and higher), estimating the Residual Motion Error (RME) is essential to ensure precise coregistration of Single-Look Complex (SLC) stacks and to generate high-quality repeat-pass interferograms. In this framework, traditional RME estimation techniques, such as the multisquint algorithm, often suffer performance degradation under large errors and high-decorrelation conditions, as they rely heavily on the quality of uncoregistered interferograms. To address this limitation, this paper introduces an enhanced version of the multisquint technique that solves the estimation of RME using a minimization strategy in the complex domain and uses the retrieved RMEs of the entire interferometric dataset to refine the estimates. This approach improves RME estimation robustness against large errors and decorrelation artifacts, particularly in high-frequency airborne and drone-borne SAR datasets. The proposed method is validated using a real dataset acquired with a Ku-band drone-borne SAR system, demonstrating significant improvements in coregistration accuracy. Quantitative evaluations based on interferometric coherence and phase fringe frequency metrics confirm its effectiveness, establishing the proposed refinement as a robust solution for high-precision interferometric applications.

This paper explores the use of Polarimetric SAR Interferometry (PolInSAR) in order to separate the Ground and Volume components of the SAR signal in the context of agricultural monitoring. This separation assumes a PolInSAR two-layer model and allows to extract the covariance matrix of each component. Then, a polarimetric change analysis technique may be employed to analyze independently the temporal evolution of the ground and volume components.The technique is evaluated with real data from the DLR’s F-SAR sensor CROPEX campaign, containing fully polarimetric PolInSAR data at L-, C- and X-band frequencies. Special attention is given to the validity of the PolInSAR two-layer volume at different frequencies and acquisition dates and the limitations of this separation technique.

This study presents a Digital Twin (DT) that is being created to optimize the use of the available hydric resources, and mitigate the effects of the increasing water shortage in irrigated agriculture in fields in the Urgell channel region (Lleida). A DT is "a virtual representation of an object or system that spans its lifecycle, it is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making." It will model the water fluxes using the knowledge of the amounts of water taken in, used, and returned to the environment, and other parameters that impact the water budget, such as atmospheric variables (temperature, water vapor deficit, relative humidity, solar radiance…), surface soil moisture, and evapotranspiration maps, etc. Satellite Earth Observation (EO) data, collocated with in-situ data from a network of 20 soil moisture probes and 2 meteo stations will be used to train the DT. Additionally, a rover-based ground penetrating radar will be used for cross-calibration.

Polarimetric synthetic aperture radar (PolSAR) can obtain rich information of ground objects through different polarization combinations, and is widely used in terrain classification. The time-varying feature of multi-temporal polarimetric SAR data is a useful supplement, which contains information that is not available in single polarimetric data. This paper aims to introduce time-varying analysis into Sentinel-1 data, so as to effectively combine the information of both time and polarization dimensions. In this way, the accuracy of crop classification using dual-polarimetric data is improved. This paper used Sentinel-1 data, firstly we constructed the dual-polarimetric coherence (DC) based on C2matrices, and analyzed the DC to find optimal time by using feature importance ranking in Random Forest model. Then, the polarimetric features and DC of the selected time are combined. Finally, the spatial correlation of MRF was utilized to classify. Compared to using only polarimetric features alone, the overall accuracy is improved.

The demonstrated efficacy of interferometric synthetic aperture radar (InSAR) techniques has spurred the development of innovative SAR satellite missions like BIOMASS and NiSAR, poised for extensive application in forest monitoring. Nevertheless, prevailing methodologies for retrieving forest variables, including forest height and above-ground biomass, encounter substantial limitations. Traditionally, successful forest mapping necessitates a non-zero spatial perpendicular baseline, full polarimetry, and a relatively small (close-to-zero) temporal baseline. This study presents a novel approach for extracting forest biophysical variables by modeling the temporal decorrelation of repeat-pass InSAR coherence. We explore a hypothesis regarding the potential relationship between the temporal decorrelation of InSAR coherence and forest variables, such as tree height and aboveground biomass. This hypothesis is tested across diverse test sites in Finland, Canada, and Germany. Our findings suggest a viable means of extracting forestry information by quantifying the temporal decorrelation of C-Band InSAR coherence. We establish a clear connection between the temporal decay rate and crucial forest variables, such as forest above-ground biomass and tree height

Catalonia has historically positioned itself as an innovative region committed to pioneering technological advancements. From the industrial revolution in the early 20th century to contemporary pursuits in the Space and Artificial Intelligence (AI) sectors, Catalonia has consistently emerged as a front-runner in Spain. Facilitated by governmental initiatives and collaborations with local institutions, such as the Space Studies Institute of Catalonia (IEEC), substantial investments and resources have been allocated to foster research and development in the realm of Artificial Intelligence technologies applied to Remote Sensing data (AI4EO). This study offers a comprehensive overview of the AI4EO ecosystem in Catalonia, encompassing (i) key stakeholders within both the public and private domains, (ii) ongoing and contemplated use cases, and (iii) an exhaustive SWOT analysis delineating the inherent strengths, weaknesses, opportunities, and threats associated with Catalonia’s engagement in AI4EO. By elucidating the current state of affairs in the AI4EO landscape, this work aims to contribute to a nuanced understanding of Catalonia’s strategic positioning in the convergence of AI and Remote Sensing technologies.

This study addresses discerning causal relationships in complex systems, a key aspect of interpretable machine learning. It focuses on the unusual and intense early summer weather in South Asia during April and May 2022 that led to an increased number of forest fires. This work employs a Bayesian network (BN), constructed using the NOTEARS algorithm, to analyse the contribution of various land and atmospheric variables on the extent of burned areas. In a scenario analysis using peak values of 300-hPa meridional circulation index and 500-hPa Geopotential Height Anomalies, indicative of a strong atmospheric block, the likelihood of large burned areas (>3.06 log ha or >1150 ha) increases from 36.6% to 41.6%. This is due to a rise of conditional probabilities in the Vapor Pressure Deficit (VPD) (> 5.21 kPa) by 24.8%, and the Land Surface Temperature (LST) (>45.7°C) by 15.6%. In addition, sensitivity and spatial analyses indicate that extreme dry conditions, characterized by high LST and VPD due to the trapping effects of the omega block jet stream pattern, were the primary factors influencing the extent of burned areas during the 2022 South Asia heatwave.

The spatial spectra of three Soil Moisture and Ocean Salinity (SMOS) soil moisture (SM) datasets, produced by the Barcelona Expert Center (BEC), were assessed in this study along zonal and meridional directions. The datasets are the Level 3 (L3) SM gridded at 25 km, the Level 4 (L4) SM at 1 km and an experimental L4 SM at ~300 m. Since the L4 products are obtained by a downscaling algorithm that uses Normalized Difference Vegetation Index (NDVI), NDVI data from MODIS (1 km) and Sentinel-3 (~300 m) were also analyzed.Both L4 SM products provide useful spatial information of small-scale structures, with estimated effective spatial resolutions of ~2.5 km (for the L4 at 1 km) and ~500 m (for the L4 at ~300 m). The NDVI data used for the downscaling have a significant impact not only on the spatial patterns of the resulting SM product, but also on its spectrum.

Multi-temporal SAR and Polarimetric SAR (PolSAR) images can provide the scattering change characteristics caused by vegetation growth to help the classifier capture phenological characteristics. To effectively utilize multi-temporal PolSAR data, a triple temporal vision transformer (TriTempoViT) model is proposed to capture correlation from multi-dimensional features. The method uses a three-branch network architecture to extract spatial-temporal, spatial-polarimetric, and temporal-polarization features respectively, and then the features from the three branches will be integrated into the vision transformer (ViT) for information interaction. Additionally, a 3D channel-spatial attention module (3DCSAM) is tailored to automatically weight the importance of the multi-dimensional feature maps. Moreover, a temporal interaction feature extraction module (TIFEM) is designed to comprehensively consider correlations between different temporal sequences. Compared with the recently developed state-of-the-art approach, the proposed method can improve OA of the classification accuracy in a Radarsat-2 dataset from Flevoland by about 0.84%, which proves the effectiveness of the proposed method.

This study presents a methodology for disaggregating the ESA Climate Change Initiative (CCI) Soil Moisture (SM) maps from 0.25° to a 60 m grid, using a feedforward neural network. This technique is applied over an area of 66,700 km2, encompassing parts of Oklahoma and Kansas (US), throughout 2021. The disaggregation approach leverages synergies between different variables, including various Sentinel-2 bands and indices, land surface temperature from MODIS, accumulated precipitation from ERA5-Land, terrain elevation and slope from the STRM, and soil composition. The methodology employs a two-step process: (i) the model is first trained using all the variables at low resolution (0.25°), and (ii) it is then employed to estimate the SM at high resolution using the input variables at 60 m. Results indicate that the model trained at low resolution achieves a reasonably high accuracy over the testing data (RMSE3•m-3and R2>0.9). The preliminary analysis of the 60 m resolution SM maps and their comparison with two in-situ stations show a strong correlation (R>0.8), and uRMSE close to 0.04 m3•m-3, with a bias ranging from 0.022 m3•m-3to 0.06 m3•m-3.

This work focuses on the feasibility of radial spatial averaging to improve satellite Synthetic Aperture Radar (SAR) ocean Horizontal Wind Speed (HWS) retrievals even in neutral atmosphere situations. The reference for comparison are in-situ HWS measurements from the FINO-1 offshore metmast. The data used from the Sentinel-1 SAR are the Level 2 Ocean (OCN) products for wind, wave, and current applications, retrieving the information from the Ocean Wind Field (OWI) component through Geophysical Model Function (GMF) CMOD-IFR2. The GMF allows for estimation of the equivalent wind speed at 10 meters and, therefore, stable and convective regimes can bias the estimations. The best agreement between SAR and FINO-1 was found -as expected- for neutral atmospheres, which yielded a determination coefficient of R2= 0.90 and a root mean squared error (RMSE) of 1.2 m/s. In non-neutral scenarios, statistical indicators showed that fine-tuned spatial averaging is also able to improve SAR HWS retrievals and yielded comparable statistical indicators (R2= 0.87, RMSE = 1.1 m/s) for the HWS estimates.

Synthetic Aperture Radar (SAR) data, particularly for Asian countries, are valuable in monitoring crops. Scattering information extracted from full-polarimetric (FP) SAR data offers exceptional sensitivity to crop water content and geometrical properties. Thus, they are widely used for continuous crop monitoring throughout their growth stages. However, many of these methods are limited to FP SAR data. This study introduces a new approach to obtain scattering power components from SLC and GRD dual-polarimetric (DP) SAR data. We found the scattering powers obtained from the dual-pol SAR data to be sensitive to changes in the crop morphology as it progresses to advanced growth stages.

Multitemporal polarimetric SAR (PolSAR) data can provide a unique insight into the temporal scattering characteristics of targets and highlight their dynamic changes over time, therefore supporting improved classification performance. Constrained by the complexities of satellite orbit control technology and the challenges associated with time-series PolSAR data acquisition, most prevailing methodologies rely solely on a single PolSAR image to tackle land coverage classification, inherently limiting their ability to generalize across diverse scenarios. To address this limitation, this work introduces a novel Hybrid Attention-GRU Transformer (HAG-Former) model, which harnesses the power of pixel-level temporal-polarimetric change analysis and captures the dynamic variations in polarization scattering properties, thereby enhancing classification robustness and versatility. In this approach, we seamlessly integrate a self-attention mechanism, a Gated Recurrent Unit (GRU), and a transformer encoder to delve into pixel-level changes in polarimetric features. Initially, the self-attention mechanism pinpoints crucial classification-aiding features, bolstering their significance. The weighted features are then fed into the GRU model, enhancing local temporal-polarimetric relationship insights. These relationships, coupled with significant features from the self-attention mechanism, are subsequently processed by the transformer encoder, unraveling global information. Furthermore, we employ a label smoothing loss function during training, mitigating the impact of sample imbalance on classification accuracy. To validate the effectiveness of our proposed methodology, we evaluated it on two benchmark datasets. The results demonstrate a notable enhancement in classification performance, achieving an overall accuracy improvement of 2.21% and 1.79% over the state-of-the-art. The code is available athttps://github.com/Thomasakun/HAGFormer.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

Vegetation optical depth (VOD) depends on the water, structure, and biomass of vegetation. Here, we propose a multi-sensor approach to isolate the water component from the VOD and to retrieve gravimetric vegetation moisture (mg) in the western United States. The approach estimates VOD from radar and LiDAR data and minimizes the differences between these estimates and SMAP/AMSR2 VOD observations. This minimization allows to obtain the best fitting value of mgwith help of a dielectric model. Results are consistent both in space (drier vegetation in arid areas) and time (drier vegetation in drier months). The mg estimates are in the same range than in situ mg data, with some underestimation (bias ~ -0.07 kg/kg). Statistical results are reasonable (r ~ 0.45, RMSE ≤0.10 kg/kg), yet the different spatial and temporal representation of in situ and remote measurements have an impact in the direct comparisons. Our results highlight the potential for developing new vegetation moisture datasets based on VOD decomposition.

In the work a random forest model has been implemented as an interpretable machine learning tool in the effort to estimate the burned areas caused by fire outbreaks in India, Pakistan, and Myanmar in April and May 2022. The proposed model combines environmental and atmospheric (including upper tropospheric) factors suggested to drive patterns of burned areas, and determines the weight of each factor on the propagation of fires. Results demonstrate that the model mimics the actual burned area by considering a combination of vegetation, atmosphere, and human-related variables and improves accuracy by approximately 7% after adding jet stream features. This approach could lead to implement a semi-operational forecast system that may be tested in multiple demonstration sites.

A Random Forest (RF) regression-tree method to derive high-resolution (60 m) surface soil moisture maps is proposed in this study. The developed methodology integrates multi-source synergies by incorporating information from the visible, near-infrared until short-wave infrared spectrum (Sentinel-2), reanalysis data (ERA5-Land) and terrain information (SRTM), using exclusively open access data. The analysis focuses on the central part of the Iberian Peninsula and covers a four-year period (2018-2021). The resulting high-resolution soil moisture maps exhibit greater spatial heterogeneity compared to the ESA Climate Change Initiative (CCI) soil moisture, which was used as a reference in the training of the RF model. These maps have been evaluated using in situ soil moisture measurements from the REMEDHUS network, and show good agreement in terms of Pearson's correlation (0.83), and uRMSE (0.028 m3•m-3), demonstrating the method’s significant potential for deriving high-resolution soil moisture information.

Multi-temporal polarimertic SAR is considered to be very effective in crop classification and cultivated land detection, which has received much attention from researchers. Currently, for most multi-temporal polarimetric SAR data classification methods, the simultaneous temporal-polarimetric-spatial feature extraction capability has not been exploited sufficiently. Also, the diversity of different time and different polarimetric features has not been taken into account sufficiently. In this paper, we propose a classification model that combines a dual-stream network as a temporal-polarimetric-spatial feature extraction module with Vision Transformer(ViT) called Temporal-Polarimetric-Spatial Transformer(TSPT) to address the above problems. Secondly, a 3 dimension(3D) convolutional attention module that enables the network to weight the temporal dimension, polarimetric feature dimension and spatial dimension is developed, according to their importance. Experimental results on both UAVSAR and RADARSAT-2 datasets show that the proposed method outperforms ResNet.

Polarimetric Synthetic Aperture Radar (SAR) data has been extensively used to estimate soil permittivity because of its high sensitivity to the dielectric properties of the target. However, the presence of vegetation cover induces bias in the permittivity estimates. This work utilizes the scattering-type parameters: alpha$(\overline{\alpha})$and theta$(\theta_{\text{FP}})$to estimate soil permittivity using the X-Bragg as the dominant surface scattering model. A theoretical study ascertains that the recently proposed$\theta_{\text{FP}}$is fairly robust towards the depolarizing component in the X-Bragg model. Hence, it is expected to produce better inversion accuracy. This study analyzes major phenology stages of Canola using the UAVSAR full-pol SAR data and the ground measurements acquired during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method achieved an RMSE of 5.9 for soil permittivity with a Pearson coefficient,$r=0.83$. Further, the temporal trend of the soil permittivity estimates also agrees with in-situ measurements for the entire timeframe.

Built-up area (BA) mapping is vital for understanding the effect of the urban regions on the environment, thereby supporting sustainable development. This study proposes a new dual-pol radar built-up area index (DpRBI) to detect the BA using Sentinel-1 SAR data. The DpRBI formulation is based on the three Stokes vector elements of the scattered wave derived from the 2 × 2 covariance matrix C2. This study uses the Sentinel-1 SAR data sets over Milan, Italy and Barcelona, Spain to map the BA using DpRBI. The overall accuracy of the BA extracted using the proposed technique was found to be 84.19% and 87.95% over Milan and Barcelona, respectively. It is noteworthy that even relatively small low-density BA is precisely classified using the proposed built-up index.

Soil permittivity estimation using Polarimetric Synthetic Aperture Radar (PolSAR) data has been an extensively researched area. Nonetheless, it provides ample scope for further improvements. The vegetation cover over the soil surface leads to a complex interaction of the incident polarized wave with the canopy and subsequently with the underlying soil surface. This paper introduces a novel methodology to estimate soil permittivity over croplands with vegetation cover using the full and compact polarimetric modes. The proposed method utilizes the full and compact polarimetric scattering-type parameters, θFPand θCP, respectively. These scattering type parameters are a function of the soil permittivity and the Barakat degree of polarization. The method considers the X-Bragg scattering model for the soil surface. In particular, these scattering-type parameters explicitly account for the depolarizing structure of the scattered wave while characterizing targets. Thus, the depolarization information in terms of surface roughness in the X-Bragg model gets inherent importance while using θFPand θCP, unlike existing scattering-type parameters. Therefore, the proposed technique enhances the expected value of the inversion accuracies. This study validated the major phenology stages of four crops using the UAVSAR full-pol and simulated compact pol SAR data and the ground truth data collected during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method estimated permittivity with an RMSE of 2.2 to 4.69 for FP and 3.28 to 5.45 for CP SAR data along with a Pearson coefficient,r≥ 0.62.

In multitemporal SAR and Polarimetric SAR (Pol-SAR) coherence is a capital parameter to exploit common information between temporal acquisitions. Yet, its use is limited to high coherences. This article proposes the analysis of low coherence scenarios by introducing a reinterpretation of coherence. It is demonstrated that coherence results from the product of two terms accounting for coherent and radiometric changes, respectively. For low coherences, the first term presents low values, preventing its exploitation for information retrieval. The information provided by the second term can be used in these circumstances to exploit common information. This second term is proposed, as an alternative to coherence, for information retrieval for low coherences. Besides, it is shown that polarimetry allows the temporal optimization of its values. To prove the benefits of this approach, multitemporal SAR and PolSAR data classification is considered as a tool, showing that improvements of the classification overall accuracy may range between 20% and 50%, compared to classification based on coherence.

Incidence angle diversity of space-borne radiometer and radar systems operating at low microwave frequencies needs to be taken into consideration to accurately estimate soil moisture (SM) across spatial scales. In this study, the Single Channel Algorithm (SCA) is first applied to SMOS brightness temperatures at vertical polarization (TBV) to estimateSMat coarse-resolution (25 km) and develop a land cover-specific and incidence angle (32.5°, 42.5° and 52.5°)-adaptive calibration of single scattering albedo (ω) and soil roughness (hs) parameters. These effective parameters are used together with fine-scale multi-angular Sentinel-1 backscatter in a single-pass active-passive downscaling approach to estimateTBVat fine-scale (1 km) for each SMOS incidence angle. TheseTBVare finally inverted to obtain the corresponding high-resolutionSMmaps. Results over the Iberian Peninsula for year 2018 show an increasing trend of ω and a decreasing trend ofhswith SMOS incidence angle, with almost no variability of ω across land cover types. The active-passive covariation parameter is shown to increase with SMOS incidence angle and decrease with Sentinel-1 incidence angle. Coarse and fineTBVmaps from the three SMOS incidence angles show similar distributions (mean differences below 0.38 K). Resulting high-resolutionSMmaps have maximum differences in mean and standard deviation of 0.016 and 0.015 m3/m3, respectively, and compare well within situmeasurements. Our results indicate that model-based microwave approaches to estimateSMcan be adequately adapted to account for the incidence angle diversity of planned missions such as CIMR, ROSE-L and Sentinel-1 next generation.

This paper provides a high level overview of a multi-frequency Ground-Based Synthetic Aperture Radar operating at X-, C-, L- and P-bands. The system core is implemented using a flexible high performance Software Defined Radio, aided by a custom radio-frequency front-end. The capabilities of the system are demonstrated by measuring dense time-series of a vegetated area. The benefits of lower frequencies appear as a significant increase in phase stability and coherences at P- and L-bands, which is explained by the increased vegetation penetration depth of these bands compared of C- and X-bands. It is concluded that multi-frequency measurements closely spaced in time are valuable and suggest new applications in vegetated areas.

Target decomposition methods for polarimetric Synthetic Aperture Radar (PolSAR) data aim at explaining the scattering information. In this regard, several conventional model-based methods use scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. This study uses the 3D Barakat degree of polarization (DoP) to obtain the scattered wave polarization state. We employ the DoP to obtain the even bounce, odd-bounce, and diffuse scattering power components. Besides, we propose a measure of target scattering asymmetry, which is subsequently utilized to obtain the helicity power. All the power components in our approach are roll-invariant and non-negative, and the decomposition preserves the total power. We utilized C-band full polarimetric RADARSAT-2 data to show the effectiveness of the proposed decomposition.

This letter addresses the use of the Sentinel-1 time series with the aim of proposing an automatic and unsupervised coastline detection method that averages the dynamical variations of coastal areas over a limited period of time, e.g., one year. First, we propose applying a temporal averaging filter that allows the temporal variations in coastal areas, e.g., due to tides or vegetation, to be encapsulated, and, at the same time, the speckle to be reduced, without decreasing the spatial resolution of the synthetic aperture radar (SAR) time series. Then, based on the distinctive backscattering values of the sea and land pixels, we will employ an iterative hierarchical tiling method in order to accurately characterize the two classes using bimodal distribution. The distribution is then segmented by a thresholding and region-growing procedure to separate the sea and land classes. A large-scale quantitative comparison between the SAR-derived and open street map (OSM) coastlines allows for a numerical evaluation of the results, i.e., an overall agreement ranging from 80% to 90%. In addition, Sentinel-2 images are used to evaluate the estimated SAR coastline qualitatively. Furthermore, the benefits of having an accurate SAR coastline are shown in the case of two well-known Earth observation-monitoring applications, ship detection, and floodwater mapping.

In this article, we present a multipolarimetric estimation approach for two model-based vegetation structure parameters (shape A and orientation distribution ψ of the main canopy elements). The approach is based on a reduced observation set of three incoherent (no phase information) polarimetric backscatter intensities (|SHH|2, |SHV|2, and |SVV|2) combined with a two-parameter (APand ψ) discrete scatterer model of vegetation. The objective is to understand whether this confined set of observations contains enough information to estimate the two vegetation structure parameters from the L-band radar signals. In order to disentangle soil and vegetation scattering influences on these signals and ultimately perform a vegetation only retrieval of vegetation shape A and orientation distribution ψ, we use the subpixel spatial heterogeneity expressed by the covariation of co- and cross-polarized backscatter ΓPP-PQof the neighboring cells and assume it is indicative for the amount of a vegetation-only co-to-cross-polarized backscatter ratio μPP-PQ. The ratio-based retrieval approach enables a relative (no absolute backscatter) estimation of the vegetation structure parameters which is more robust compared to retrievals with absolute terms. The application of the developed algorithm on global L-band Soil Moisture Active Passive (SMAP) radar data acquired from April to July 2015 indicates the potential and limitations of estimating these two parameters when no fully polarimetric data are available. A focus study on six different regions of interest, spanning land cover from barren land to tropical rainforest, shows a steady increase in orientation distribution toward randomly oriented volumes and a continuous decrease in shape arriving at dipoles for tropical vegetation. A comparison with independent data sets of vegetation height and above-ground biomass confirms this consistent and meaningful retrieval of APand ψ. The retrieved shapes and orientation distributions represent the main vegetation elements matching the literature results from model-based decompositions of fully polarimetric L-band data at the SMAP spatial resolution. Based on our findings, APand ψ can be directly applied for parameterizing the vegetation scattering component of model-based polarimetric decompositions. This should facilitate decomposition into ground and vegetation scattering components and improve the retrieval of soil parameters (moisture and roughness) under vegetation.

This research addresses the use of Sentinel-1 time series with the aim of detecting spatio-temporal changes in the coastal environment. To this end an automatic and unsupervised coastline detection method is proposed. First, we apply a temporal averaging filter that allows encapsulating the temporal variations in coastal areas, e.g. due to tides or vegetation, and at the same time it allows reducing the speckle, without decreasing the spatial resolution of the Synthetic Aperture Radar (SAR) images. Then, based on the distinctive backscattering values of the sea and land classes we employ an iterative hierarchical tiling method in order to accurately characterize the two classes by a bimodal distribution. The latter is then segmented by a thresholding and region-growing procedure to separate the sea and land classes. The proposed method is applied to two different SAR time-series, each one acquired throughout one year. The extracted yearly coastlines are then analyzed in order to identify spatio-temporal changes. Experimental results showcase coastal area changes between occuring 2018 and 2019 and that were caused by the hurricane Michael hitting Northwest Florida in October 2018.

This article proposes a change detection and analysis technique for monitoring the phenological development of agricultural vegetation by means of multitemporal Polarimetric Synthetic Aperture Radar (PolSAR) acquisitions. The technique relies on the generalized eigendecomposition of the polarimetric covariance matrices of the individual acquisitions. It both quantifies the magnitude of the change between PolSAR images acquired at different times and also provides an interpretation of occurred change in terms of the modified polarization states. This makes the algorithm suitable for investigating scattering dynamics associated with the phenological development of agricultural vegetation. To aid the interpretation of the changes detected, a representation based on the polarization states affected by the change process is proposed. The technique is evaluated using part of the multitemporal AGRISAR 2006 campaign data set. This data set consists of 12 quad-polarimetric images acquired by the German Aerospace Center (DLR) E-SAR airborne system at L-band from April 2006 to August 2006 over the Demmin test site. It covers large parts of the development cycle of different crop types. As a part of the evaluation, reference ground measurements are used to facilitate the interpretation of the data. The evaluation focuses on five important crop types: wheat, barley, rape, maize, and sugar beet. The results show that the proposed technique is able to detect and characterize different types of changes related to distinct development states of different crop types as the plant growing, maturation, and drying processes.

Since its first flight in 2007, the UAVSAR instrument of NASA has acquired a large number of fully Polarimetric SAR (PolSAR) data in very high spatial resolution. It is possible to observe small spatial features in this type of data, offering the opportunity to explore structures in the images. In general, the structured scenes would present multimodal or spiky histograms. The finite mixture model has great advantages in modeling this kind of data. In this paper, a type of important statistics called log-cumulants are derived for the finite mixture model. They are adopted to UAVSAR data analysis to determine statistical behaviors of different land types.

This study addresses the use of Sentinel-1 data for innovative improvements of automatic classic ship detection detection chains. Firstly, we propose to extract the complex coherence from the two polarization channels and to perform the vessel detection the vessels in this domain. A comparative assessment between the use of the complex coherence and the intensity images together with AIS validation demonstrates that the fusion of the different results allows to reduce the number of false alarms while maintaining an optimal detection rate. Secondly, we propose to make use of Sentinel-1 time series in order to delineate the coastline which is an essential parameter for ship detection chains. Experimental results are conducted on Sentinel-1 images acquired in different areas of interest for maritime surveillance such as the Gulf of Califoria (Mexico) or the English Channel.

This paper discusses an image processing architecture and tools to address the problem of ship detection in synthetic-aperture radar images. The detection strategy relies on a tree-based representation of images, here a Maxtree, and graph signal processing tools. Radiometric as well as geometric attributes are evaluated and associated with the Maxtree nodes. They form graph attribute signals which are processed with graph filters. The goal of this filtering step is to exploit the correlation existing between attribute values on neighboring tree nodes. Considering that trees are specific graphs where the connectivity toward ancestors and descendants may have a different meaning, we analyze several linear, nonlinear, and morphological filtering strategies. Beside graph filters, two new filtering notions emerge from this analysis: tree and branch filters. Finally, we discuss a ship detection architecture that involves graph signal filters and machine learning tools. This architecture demonstrates the interest of applying graph signal processing tools on the tree-based representation of images and of going beyond classical graph filters. The resulting approach significantly outperforms state-of-the-art algorithms. Finally, a MATLAB toolbox allowing users to experiment with the tools discussed in this paper on Maxtree or Mintree has been created and made public.

Microwaves are expected to retrieve soil conditions in agricultural lands when an overlying crop canopy is presented. Since crop plants involve dielectric elements of complex shapes, sizes and orientations, a number of scattering mechanisms combine together to yield the microwave backscatter response. This paper aims at assessing soil moisture variations through a canopy imaged by an airborne Synthetic Aperture Radar (SAR). Multi-temporal co-polarized and cross-polarized response is compared to volumetric insitu soil moisture for two crops with contrasting biomass and plant height, namely soybean and corn. The considered dataset is comprised by several soybean and corn fields, whose response to microwaves is averaged leading to an overall assessment for each crop. For canopies with large stalks such as corn, observations suggest an important contribution of the double bounce mechanism when soil surface is wet. For drier conditions, response is insensitive to moisture variations in accordance to backscattering from plant elements. Results might be useful for crops with similar features such as wheat and rapeseed.

This study aims to map built-up areas using SAR data provided by the Sentinel-1 mission. The proposed algorithm exploits several features offered by the satellite mission such as: high revisit time, dual-polarization data and Interferometric SAR coherence. The algorithm is based on an adaptive parametric thresholding methodology that identifies pixels with high backscattering values in both VV and VH channels corresponding to built-up areas. The Interferometric SAR coherence allows discriminating false alarms caused by other land cover classes characterized by high backscattering values which are not coherent in time (e.g. certain types of vegetated areas). Both the intensity in VV and VH, as well as coherence features are obtained by averaging multi-temporal SAR series. This allows reducing the speckle without any loss in spatial resolution. The algorithm has been tested on Sentinel-1 Interferometric Wide Swath data from five different test sites located in semiarid and arid regions in the Mediterranean region and Northern Africa.

In the last three decades, a considerable research effort has been devoted to find accurate statistical models for Polarimetric SAR data, and a number of distributions have been proposed. In this paper, different statistical models organized in four categories: Gaussian distributions, product models, finite mixture models, and copula based models, are reviewed. A comparison is drawn to show the advantages of different models.

Soil moisture under grassland is assessed at field scale using multiple sensing techniques: in situ soil moisture network measurements (SoilNet), rover-based cosmic ray neutron sensing (CRNS rover) and airborne polarimetric SAR acquisitions (PolSAR) at L-band. The three interdisciplinary techniques acquire on different spatial scales from meters to hectometers. In this study, the methods are blended at the field scale to estimate soil moisture under grassland in a synergistic as well as a stand-alone approach. Data from the TERENO Fendt test site near Weilheim (Germany) were recorded concurrently within the ScaleX campaign on 10thof July, 2015. The multisensor assessment reveals that PolSAR estimates benefit fundamentally from the in situ techniques to effectively remove the vegetation scattering component leading to very accurate permittivity estimates (RMSE <; 1 [-]). The PolSAR analyses verified the full applicability of the low-parameterized vegetation scattering model to sufficiently represent grassland cover. Moreover, the comparison of all moisture products indicates the constraint of PolSAR to assess only the surface moisture at L-band, while the other two techniques are able to assess also soil moisture of deeper layers, reaching down to the root zone.

This study addresses the use of dual-polarimetric descriptors for ship detection and characterization from Synthetic Aperture Radar (SAR) data. Ship detection is usually performed independently on each polarization channel and the results are merged subsequently. We propose to extract polarimetric descriptors from the two polarization channels and to perform the vessel detection the vessels in this domain. Several polarimetric descriptors, such as those derived from the the Eigenvector-Eigenvalue decomposition, are employed for this purpose. A comparative assessment between the use of intensity images and polarimetric descriptors for the detection and identification of ships is then realized. The proposed methodology is tested on Sentinel-1 data acquired over the English channel. Automatic Identification System (AIS) data flows are considered as ground truth.

InSAR coherence is a promising parameter for land-cover classification and mapping. The ESA SEOM SInCohMap project is devised to test and analyze multi-temporal InSAR coherence potentialities exploiting dense multitemporal data from the Sentinel-1 constellation. In the framework of the project, this paper shows the first classification results using machine learning algorithms over a two-year period of InSAR coherence data. The evaluation is performed on the test site of Doñana (Seville, Southwestern Spain), mainly an agricultural area where different land covers can be identified. Classification results exploiting InSAR coherence shows accuracies around 80 % for this site.

In this paper, we introduce two spatially adaptive filtering methods to improve the estimation of the covariance matrix (CM), which is required for the processing of tomographic SAR data. We evaluate their effect on scatterer separation and height estimation. We propose several criteria to evaluate such methods and introduce a spatial simulation procedure allowing generating a tomographic image stack from a 3-D building model, assuming a multitrack airborne configuration and a distributed target model incorporating multidimensional speckle. Inversion of such a model requires the estimation of a CM from the data. Consequently, we propose two nonlocal methods to improve the estimation of the CM. The first one was previously introduced for polarimetric data and uses pixel similarities based on Riemannian distances between CMs. The second one is a new method extending the previous one to similarities between patches. We show the importance of spatial adaptivity in covariance estimation by comparing the 3-D reconstructions obtained with our filters and other methods. Further experiments on simulated and L-band experimental data show the ability of the nonlocal filters to improve the height estimation and scatterer separation in layover areas thanks to their smoothing and edge-preserving properties.

In this paper, we introduce two spatially adaptive covariance filtering methods and evaluate their effect on scatterer separation and height estimation from tomographic SAR. The first one was previously introduced for polarimetric data and uses pixel similarities based on Riemannian distances between covariance matrices. The second one is a new method extending the previous one to patch-based similarities. We show the importance of spatial adaptivity in covariance estimation by comparing the 3D reconstructions obtained with our nonlocal filters and the boxcar filter. Our experiments on simulated and L-band experimental data show the ability of the non-local filters to improve the height estimation and scatterer separation in layover areas thanks to their smoothing and edge preserving properties.

The present work is oriented to analyze the backscattering signal generated by the X-band, when it is interacting with sea ice, located in the vicinity of Fildes Bay, King George Island (Southland Shetland Islands), from Antarctic. Radar images captured by TerraSAR-X satellite StripMap mode were used, whose emitted signal was polarized in Dual mode (HH-VV). The images were processed using the software Polsar-Pro, with the objective of establishing Polarimetric Decomposition and Shannon Entropy, in order to determine the predominant mechanisms of dispersion and to corroborate with information obtained in situ. The results demonstrated that both techniques are complementary and individually allow detecting and characterizing the presence of sea ice in this Antarctic zone.

Next L-band fully polarimetric Synthetic Aperture Radar (SAR) missions will provide meaningful and timely data over large agricultural areas. The purpose of this work is to evaluate the potential of L-band PolSAR (SAR polarimetry) for crop monitoring using incoherent target decomposition theorems applied to PolSAR data from the NASA/JPL UAVSAR airborne system over Canada. Polarimetric parameters Entropy, Mean Alpha Angle and Anisotropy are related to soil and vegetation water content, plant structure parameters (height and diameter) and the water distribution on the different plant parts (leaves, trunks, etc). Results are expected to contribute to the quantitative retrieval of physical parameters over croplands.

The log-cumulants of the second and third order are widely used to determine the statistical model of PolSAR data. However, same values of these statistics could result from both the product model and the mixture model, which represent two different physical scenarios. In other words, there is an ambiguity between the texture and the mixture according to these statistics. In this work, the log-cumulant of the fourth order is demonstrated to be useful to eliminate this ambiguity. The use of higher order statistics is helpful and necessary when analyzing the texture of PolSAR data.

Speckle noise is an inherent problem in synthetic aperture radar (SAR) imaging system. For further imagery analysis and interpretation, it demands better and more efficient polarimetric SAR (PolSAR) speckle-filtering algorithms. Inspired by the great success of stochastic denoising, our goal in this paper is to reduce speckle noise of PolSAR image with random walk model. Taking spatial distance into consideration, limited steps of random walks over small pixel neighbourhoods provides good stochastic samples, then the final estimation of denoised image based on transition probabilities is obtained. In addition, since polarimetric distance is used to measure the dissimilarities between pixels, the proposed method exploits both the spatial and polarimetric information. Despeckling results on simulated SAR data and real SAR data demonstrate that our method provides efficient speckle reduction as well as spatial resolution and polarimetric information preservation.

The logarithmic cumulants (log-cumulants for short) of the second and third orders are widely used in the statistical analysis of polarimetric synthetic aperture radar (PolSAR) data. However, both the product model and the finite mixture model may produce the same values of these statistics, which means that the use of these log-cumulants is not enough to determine the statistical model of the data. In this letter, it is demonstrated that the log-cumulants of higher orders can help to distinguish the concept of texture from that of mixture, providing a physical insight into the data statistics. A tool called log-cumulant cube, which helps to visualize this difference, is proposed by considering texture distributions from the Pearson's family. Results on both simulated and real SAR data show that the use of higher order statistics is useful when it comes to the texture analysis of PolSAR data.

In this paper, the use of the l2-norm, or Span, of the scattering vectors is suggested for texture analysis of polarimetric synthetic aperture radar (SAR) data, with the benefits that we need neither an analysis of the polarimetric channels separately nor a filtering of the data to analyze the statistics. Based on the product model, the distribution of the l2-norm is studied. Closed expressions of the probability density functions under the assumptions of several texture distributions are provided. To utilize the statistical properties of the l2-norm, quantities including normalized moments and log-cumulants are derived, along with corresponding estimators and estimation variances. Results on both simulated and real SAR data show that the use of statistics based on the l2-norm brings advantages in several aspects with respect to the normalized intensity moments and matrix variate log-cumulants.

The random walk model is studied with the objective to obtain a physical explanation for the texture of polarimetric synthetic aperture radar (SAR) data. A simulator is designed to imitate the scattering process under different circumstances, taking into account different distributions for the scatterer number and the scatterer response, as well as mixtures of scatterers. Statistical analysis of the simulated data shows that the distribution of the scatterer response has an effect only when the number of scatterers in a resolution cell is very small, which appears in very high resolution data. Moreover, it is found that both the fluctuation of the scatterer number and the mixture of different targets can give non-Gaussian-distributed data. The mixture of point targets and distributed targets will lead to an extremely heterogeneous appearance, which may be a clue to analyze the urban areas in polarimetric SAR data.

This paper presents a detailed characterization of sea/ocean clutter returns at X-band, imaged by TerraSAR-X (TSX) mission from the radiometric, statistical, and polarimetric standpoints. Different TSX data takes, covering the typical spaceborne incidence angle region (20°-45°), are analyzed: dual-polarized (dual-pol) and experimental quad-polarized (quad-pol) data have been used. The thermal noise of the receiver for quad-pol data turns out to be an important limitation in the sea characterization from TSX, particularly at high incidence angles (above 36°), where low signal-to-noise ratios (SNRs) can impair the proper data distribution fitting and the polarimetric backscattering description. Statistical analysis shows large deviation from Gaussianity, indicating presence of texture, mainly in the small incidence region (20°-36°), with favorable SNR conditions. The different polarimetric features revealed contribution of nonpolarized scattering, related to the presence of breaking waves. This paper also proposes and evaluates an extension of the well-known X-Bragg model, named X2-Bragg (extended-extended Bragg), which properly accounts for the impact of thermal noise and sea clutter temporal decorrelation, due to the dual receive antenna (DRA) acquisition mode. Such additional decorrelation sources, if not properly analyzed and accounted for in the physical-based model description, could lead to an incorrect interpretation of the polarimetric properties and the related erroneous geophysical parametric inversion from the real data. In this sense, the X2-Bragg proves its fitting to the experimental data, quantifying accordingly the presence of nonpolarized scattering in terms of the roughness parameter β1.

In this paper, the scattering model, also known as random walk model, is studied with the objective to obtain a physical explanation for the texture of polarimetric SAR data. Statistical analysis of the simulated data shows that both the fluctuation of the scatterer number and the mixture of different scatterers can give non-Gaussian distributed data. The mixture of scatterers will lead to extremely heterogeneous appearance, which may be a clue to analyze the urban areas in polarimetric SAR data. Besides, the distribution of the scatterer response has effect only when the number of scatterers in a resolution cell is small, which appears in very high resolution data.

In this study application results are presented derived from multi-parametric SAR observations covering five different thematic domains: forest, agriculture, ocean, urban and cryosphere. In total 21 application products have been selected and described. Their application on different data sets, space- and airborne sensors, was demonstrated and can independently be reproduced by any scientist. The results and algorithms are available soon through Springer.

This paper shows the necessity to include the impact of instrument-driven constrains in the physical modeling of polarimetric data from spaceborne SAR missions, specifically for TerraSAR-X (TSX) case. The intrinsic acquisition of fully (quad-pol) polarimetric data, exploiting the dual-receive antenna (DRA) mode, may induce two additional sources of decorrelation when observing challenging dynamic scenarios, such as the ocean or sea: limited signal-to-noise ratio (SNR) as the receive antenna is halved and temporal decor-relation induced over the along-track configuration (spatial baseline between the polarimetic channels) due to internal clutter motion. Experimental quad-pol TSX data over the ocean has been used to study the applicability of the original X-Bragg model as well as its extension accounting for these system/scenario dependent limitations, which shows a much better fitting.

Measuring the pairwise similarity/dissimilarity of data is of central importance for processing and analyzing PolSAR images. In the literature, a large variety of measurements have been used, however, it is still not clear how to choose appropriate similarity measures for a given task. This paper presents a brief summary and discussion of the dissimilarity measurements used for interpreting PolSAR Images.

The application of differential synthetic aperture radar interferometry (DInSAR) techniques has been traditionally limited to the single-polarimetric case. The launch of satellites with polarimetric capabilities has triggered the synergy between polarimetric and interferometric algorithms, leading to a significant improvement in final DInSAR products. During the last years, the different polarimetric optimization techniques available have been successfully applied to the so-called classical phase quality estimators, i.e., the coherence and the amplitude dispersion estimators. In this context, a new estimator to evaluate the pixels' phase quality, referred to as temporal sublook coherence (TSC), has recently demonstrated to provide promising results in DInSAR applications. The nature of this estimator, which is based on exploiting the spectral properties of pointlike scatterers through the coherence evaluation of different sublooks of the image spectrum, allows its adaptation to the existing polarimetric optimization methods. This letter presents the benefits of extending the TSC estimator to work with fully polarimetric data. For this purpose, a fully polarimetric data set consisting of ten X-band ground-based SAR (GB-SAR) images is employed. The final DInSAR results obtained by means of TSC polarimetric optimization are compared with the ones obtained with its classical single-polarimetric approach, achieving up to more than a twofold increase in the pixels' density.

This work focuses on the exploitation of PolSAR and PolInSAR temporal series datasets in the context of change detection and characterization. Instead of a classical pixel-based approach, a Binary Partition Tree (BPT) data structure is employed to perform a region-based processing of the image. Once the homogeneous regions of the data set are obtained, the temporal dimension of the data is analyzed to quantify the significance of the polarimetric temporal variation. In this work, the PolInSAR information will also be exploited for this purpose, in order to achieve a more detailed characterization of change and no-change areas.

Speckle noise represents one of the main components of the multidimensional SAR signal that limit the complete exploitation of multidimensional SAR data, and polarimetric SAR data in particular. This paper consider the main trends in the characterization and the filtering of the multidimensional Speckle noise component.

This paper discusses the interest of Binary Partition Trees (BPTs) and the usefulness of graph cuts for low-level processing of PolSAR images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for many applications including filtering, segmentation, classification and object detection. Many processing strategies consist in populating the tree with a specific feature and in applying a graph-cut called pruning. Different graph-cuts are discussed and analyzed in the context of PolSAR images for speckle filtering and segmentation.

This paper deals with the processing of polarimetric synthetic aperture radar (SAR) time series. Different approaches to deal with the temporal dimension of the data are considered, which are derived from different target characterizations in this dimension. These approaches are the basis for defining two different binary partition tree (BPT) structures that are employed for SAR polarimetry (PolSAR) data processing. Once constructed, the BPT is processed by a tree pruning, producing a set of spatiotemporal homogeneous regions, and estimating the polarimetric response within them. It is demonstrated that the proposed technique preserves the PolSAR information in the spatial and the temporal domains without introducing bias nor distortion. Additionally, the evolution of the data in the temporal dimension is also analyzed, and techniques to obtain BPT-based scene change maps are defined. Finally, the proposed techniques are employed to process two real RADARSAT-2 data sets.

In this paper, a new model-based technique for the compensation of severe height-dependent atmospheric artifacts, using ground-based synthetic aperture radar (SAR) data over mountainous regions, is proposed. The method presented represents an extension of already existing techniques, but now taking into account the effect of steep topography in the atmospheric phase screen compensation process. In addition, the technique is adapted to work with polarimetric SAR data, showing, in that case, a noticeable improvement in the compensation process. The method is validated in the mountainous environment of El Forn de Canillo, located in the Andorran Pyrenees, where there is a slow-moving landslide that nowadays is being reactivated coinciding with strong rain episodes. In this framework, ten zero-baseline fully polarimetric data sets have been acquired at X-band during a one-year measurement campaign (October 2010-October 2011) with the GB-SAR sensor developed at the Universitat Politècnica de Catalunya. First, the impact of the severe atmospheric fluctuations among multitemporal GB-SAR measurements is carefully studied and analyzed. Hence, the need to correctly estimate and compensate the resulting phase differences when retrieving interferometric information is put forward in the frame of differential-SAR-interferometry applications.

In this paper, a study of polarimetric optimization techniques in the frame of differential synthetic aperture radar (SAR) interferometry (DInSAR) is considered. Historically, DInSAR techniques have been limited to the single-polarimetric case, mainly due to the unavailability of fully polarimetric data. Lately, the launch of satellites with polarimetric capabilities, such as the Advanced Land Observing Satellite (ALOS), RADARSAT-2, or TerraSAR-X, allowed merging polarimetric and interferometric techniques to improve the pixels' phase quality and, thus, the density and the reliability of the final DInSAR results. The relationship between the polarimetrically optimized coherence or amplitude dispersion maps and the final DInSAR results is carefully analyzed, using both orbital and ground-based SAR fully polarimetric data. DInSAR processing using polarimetric optimization techniques in the pixel selection process is compared with the classical single-polarimetric approach, achieving up to a threefold increase of the number of pixel candidates in the coherence case and up to a factor of seven in the amplitude dispersion case.

This paper investigates the validity of the random-volume-over-ground (RVoG) scattering model assumption for forest scattering on polarimetric interferometric synthetic aperture radar (PolInSAR) data. The model makes some assumptions about the data and the structure of coherency matrices, namely, the equality of the polarimetric covariance matrices and the affine equivalence of the contracted polarimetric interferometric covariance matrix with a Hermitian matrix. The proposed methodology is divided into two main steps. First, invertible affine transforms (ATs) are studied and proposed as a tool to operate with coherence regions. Based on this analysis, the concept of the trace matrix is introduced as its rank depends on the RVoG model assumption validity. Then, with the objective to consider the effects of speckle noise, we consider a maximum-likelihood (ML) framework, on the hypothesis of data distributed according to the complex Gaussian distribution. Hence, we define the ML estimator (MLE) of the PolInSAR coherency matrix according to the RVoG model assumption and the generalized likelihood ratio test of the model. The validity tests and the MLE are analyzed in terms of simulated and real PolInSAR data, considering P-band and L-band data over tropical and boreal forests.

A novel analysis of the statistics of the eigendecomposition of the coherency matrix and the H/A/a¯ parameters of polarimetric synthetic aperture radar data is addressed. The objective is to overcome previous approaches that prevented the extraction of information about the sample eigenvectors or restricted the analysis to simulated data. This paper considers a perturbation analysis of the eigendecomposition of the coherency matrix, making it possible to obtain analytical expressions for the sample eigenvalues and their means and variances, the sample mean entropy and anisotropy, the sample eigenvectors and the sample ai angles, as well as for the sample mean alpha angle a¯. All the parameters are shown to be estimated asymptotically non-biased with respect to the number of averaged samples. It is also demonstrated that the sample eigenvectors are more robust than the sample eigenvalues to the presence of speckle. Finally, a simple technique for the precise removal of the entropy bias is presented.

Speckle noise filtering on polarimetric SAR (PolSAR) images remains a challenging task due to the difficulty to reduce a scatterer-dependent noise while preserving the polarimetric information and the spatial information. This challenge is particularly acute on single look complex images, where little information about the scattering process can be derived from a rank-1 covariance matrix. This paper proposes to analyze and to evaluate the performances of a set of PolSAR speckle filters. The filter performances are measured by a set of ten different indicators, including relative errors on incoherent target decomposition parameters, coherences, polarimetric signatures, point target, and edge preservation. The result is a performance profile for each individual filter. The methodology consists of simulating a set of artificial PolSAR images on which the various filters will be evaluated. The image morphology is stochastic and determined by a Markov random field and the number of scattering classes is allowed to vary so that we can explore a large range of image configurations. Evaluation on real PolSAR images is also considered. Results show that filters performances need to be assessed using a complete set of indicators, including distributed scatterer parameters, radiometric parameters, and spatial information preservation.

In this paper the exploitation of PolSAR temporal series datasets is presentedin the context of change detection and characterization. A Binary Partition Tree (BPT) data structure is employed in order to extract homogeneous regions of the image containing pixels that are following a similar polarimetric temporal evolution. Then the temporal dimension of the data is analyzed firstly to quantify the significance of the polarimetric temporal variation, making possible the detection of scene changes, and secondly to analyze and characterize those changes. Finally, the proposed technique is employed to process a real RADARSAT-2 dataset to show its capabilities and potentialities.

This work addresses the analysis of the validity of the Random-Volume-over-Ground scattering model for forests studies based on PolInSAR data. The analysis is based on the definition of a Generalized Likelihood Ratio Test that allows to test the model validity for all the pixels of the images and without any external information. Finally, the validity of the RVoG models assumption is tested for data from Tropical and Boreal forests imaged at P- and L-band at different interferometric configurations.

The eigendecomposition of the coherency matrix, as well as the Entropy, Anisotropy and mean Alpha angle are crucial for the physical understanding of the scattered echo in SAR polarimetry. This contribution considers a perturbation analysis of the coherency matrix that allows the statistical characterization of all the previous parameters. In addition, a novel and effective algorithm for the unbiased estimation of the Entropy parameter is presented.

The current increase of spatial as well as spectral resolutions of modern remote sensing sensors represents a real opportunity for many practical applications but also generates important challenges in terms of image processing. In particular, the spatial correlation between pixels and/or the spectral correlation between spectral bands of a given pixel cannot be ignored. The traditional pixel-based representation of images does not facilitate the handling of these correlations. In this paper, we discuss the interest of a particular hierarchical region-based representation of images based on binary partition tree (BPT). This representation approach is very flexible as it can be applied to any type of image. Here both optical and radar images will be discussed. Moreover, once the image representation is computed, it can be used for many different applications. Filtering, segmentation, and classification will be detailed in this paper. In all cases, the interest of the BPT representation over the classical pixel-based representation will be highlighted.

This paper investigates the polarimetric dependence of the interferometric complex correlation and proposes a methodology for cancelling individual scattering mechanisms, in terms of the complex correlation coefficient phase, under the assumption of the random volume over ground model. This allows the estimation of the ground topography on forested and vegetated areas. The first part of the analysis considers the separation of the volume from the ground (including the double-bounce scattering mechanism). This process identifies the polarization states, without constraining them to be equal in both polarimetric acquisitions, which allow to cancel either the volume scattering contribution or the ground contribution. In order to have access to the interferometric phase of the remaining or isolated scattering mechanism, the polarimetric phase contribution of this scattering mechanism has to be removed in a second step. In the case of forested areas, the previous methodology is considered from two different point of views. For the estimation of the underlying ground topography, the cancellation of the volume scattering contribution makes possible to access the interferometric phase associated to the ground contribution. In addition, the interferometric information associated to the volume scattering contribution is estimated based on the cancellation of the ground contribution. The proposed techniques are analyzed on the basis of simulated and experimental polarimetric interferometric synthetic aperture radar data, demonstrating that the ground topography, as well as the height associated to the volume contribution, are asymptotically nonbiased and dependent on the shape of the particles of the random volume. In case of spheres$(\eta = 0)$, the ground-to-volume ratio presents large values favoring the accurate estimation of the topographic phase. For the case of dipole like particles$(\eta = 0.5)$, the ground-to-volume ration decreases producing a coherence$\vert\rho\vert$in the order of 0.1, making necessary a large speckle filtering to obtain a reliable estimation of the topographic phase.

Revisiting time constitutes a key constraint for continuous monitoring activities based on space- and airborne synthetic aperture radar (SAR) acquisitions. Conversely, the employment of terrestrial platforms overcomes this limitation and makes it possible to perform time-continuous observations of small space-scale phenomena. New research lines of SAR dealing with the backscattering evolution of different types of scenarios become hence possible through the analysis of ground-based SAR (gbSAR) data collections. The Remote Sensing Laboratory of the Universitat Politècnica de Catalunya drove a one-year measurements campaign in the village of Sallent, northeastern Spain, using its X-Band gbSAR sensor. The field experiment aimed at studying the subsidence phenomenon induced by the salt mining activity carried out in this area during the past decades. In this paper, the polarimetric behavior of an urban environment is investigated at different time scales. After a brief description of the test site and the measurement campaign, the analysis is focused on the stability on man-made structures at different time scales. PolSAR data monthly acquired from June 2006 to July 2007 are employed to stress the presence of nonstationary backscattering processes within the urban scene and the effect they have on differential phase information. Then, a filtering procedure aiming at reducing backscattering randomness in one-day and long-term data collections is then put forward. The improvements provided by the proposed technique are assessed using a new polarimetric descriptor, the time entropy. In the end, the importance of preserving the interferometric phase information from nonstationary backscattering contaminations using fully polarimetric data is discussed.

In this paper, the processing of temporal PolSAR image series is addressed through a region-based and multi-scale data representation, the Binary Partition Tree (BPT). This structure contains useful information related to the data structure at different detail levels that may be employed for different applications. The construction of this structure ans its exploitation is addressed in this work in the context of the speckle filtering and data segmentation applications. A new region model and processing strategy are defined to tackle with the temporal dimension of the data. Finally, to illustrate the capabilities of the proposed technique, results are shown with a real RADARSAT-2 dataset.

This paper presents a Polarimetric SAR data speckle filtering technique, based on a combined filtering in the spatial and polarimetric domains. It is based on a bilateral filtering employing distance measures over these domains. These measures concentrate all the information related to the domain structure that is needed for an adaptation to the scene morphology. A weighted average is performed over a given window favoring closer and similar pixels. As a consequence, an adaptive filtering is achieved, attaining higher filtering over homogeneous areas whereas point scatters remain almost unchanged. Results will be shown over a real RADARSAT-2 data.

We are proposing recursive filter for the filtering of Single Look Complex PolInSAR data. This recursive filter allows the iterative refinement of a coarse multilook estimate by reintroducing details that have been oversmoothed. We investigate if wether or not information about the image structure (i.e. spatial details) could be recovered from all the terms of the PolInSAR matrix. Preliminary results on simulated images show also that bias on various polarimetric parameters are decreasing with iterations.

The goal of this study is to evaluate some polarimetric speckle filters when applied to Compact Polarimetry (CP) data. Seven filters are evaluated on a set of artificial CP images. Performances regarding image restoration and polarimetric information preservation are evaluated.

In this paper, the study of polarimetric optimization techniques for Differential SAR Interferometry (DInSAR) applications is analyzed. This work has been carried out in the framework of deformation map retrieval on landslides. A large number of landslides occur on vegetated areas with a poor density of temporal coherent scatterers, which are characterized by a fast decorrelation at X-band. The objective of the techniques proposed in this paper is to increase the number of temporal coherent scatterers to improve the robustness of the DInSAR algorithms exploiting the polarimetric capabilities of data. The relationship between optimum coherences and its corresponding phase quality in terms of DInSAR application is analyzed using Ground-Based SAR zero-baseline fully-polarimetric data.

This paper defines a new operator, named Ds, for local texture stationarity assessment on SAR images.. The aim is to discriminate heterogeneous targets from land cover types of high normalized variance values, as those observed in flooded vegetation areas of Doñana wetlands. Suitable Ds thresholds for such discrimination were estimated for different window sizes through Monte Carlo simulations of synthetic textures. Maximum stationary texture windows were then determined on Doñana ASAR scenes by Ds multi-resolution thresholding and averaging was applied within. Results reveal the substantial degree of smoothing achieved over high variance cover types, while edges among different targets were properly preserved.

Orbital Differential SAR Interferometry (DInSAR) is a well-known technique to retrieve terrain deformation phenomena from wide areas with high resolution. Historically its application has been limited to single polarization SAR, mainly due to the unavailability of polarimetric data. Lately, the launch of several satellites with polarimetric capabilities, such as Radarsat-2 or TerraSAR-X, allows merging polarimetric and interferometric techniques in order to improve the results obtained in the DInSAR processing. This work will explore the existent analytical techniques in order to optimize the quality of the subsidence results. The dataset used contains 35 Fine Quad-Pol Radarsat-2 acquisitions over the city of Barcelona (Spain).

In this paper,we propose the use of binary partition trees (BPT) to introduce a novel region-based and multi-scale polarimetric SAR (PolSAR) data representation. The BPT structure represents homogeneous regions in the data at different detail levels. The construction process of the BPT is based, firstly, on a region model able to represent the homogeneous areas, and, secondly, on a dissimilarity measure in order to identify similar areas and define the merging sequence. Depending on the final application, a BPT pruning strategy needs to be introduced. In this paper, we focus on the application of BPT PolSAR data representation for speckle noise filtering and data segmentation on the basis of the Gaussian hypothesis, where the average covariance or coherency matrices are considered as a region model. We introduce and quantitatively analyze different dissimilarity measures. In this case, and with the objective to be sensitive to the complete polarimetric information under the Gaussian hypothesis, dissimilarity measures considering the complete covariance or coherency matrices are employed. When confronted to PolSAR speckle filtering, two pruning strategies are detailed and evaluated. As presented, the BPT PolSAR speckle filter defined filters data according to the complete polarimetric information. As shown, this novel filtering approach is able to achieve very strong filtering while preserving the spatial resolution and the polarimetric information. Finally, the BPT representation structure is employed for high spatial resolution image segmentation applied to coastline detection. The analyses detailed in this work are based on simulated, as well as on real PolSAR data acquired by the ESAR system of DLR and the RADARSAT-2 system.

The aim of this paper is to present a Polarimetric Synthetic Aperture Radar data processing technique on the space-time domain. This approach is based on a Binary Partition Tree (BPT), which is a region-based and multi-scale data representation. Results with series of RADARSAT-2 real data are analyzed from the point of view of speckle filtering and change detection applications, to illustrate the capabilities to detect and preserve spatial and temporal contours.

This paper analyzes the effects of speckle filtering on polarimetric SAR decomposition and classification. We compared the results of the refined Lee, ID AN and Non-Local Polarimetric filters, and discussed their influence on the Cloude-Pottier decomposition and the Wishart H/α classification. ALOS/PALSAR and RadarSat-2 polarimetric SAR data are used for illustration.

Polarimetric SAR Interferometry makes possible a detailed analysis of the volumetric scattering processes present in microwave scattering in case of forest areas. In previous contributions, the authors, under the hypothesis of the RVoG coherent scattering model, developed a process for the direct extraction of the ground topography. In this paper, the authors present a generalization of this technique allowing to determine a set of scattering mechanisms making possible an optimization process for the estimation of the ground topography. The applicability of this technique, in case of X-band polarimetric SAR interferometric data, is explored considering data obtained in the frame of the TanDEM-X mission.

The use of a low-profile, lightweight, and easy-to-fabricate transpolarizing surface placed on one side of a trihedral corner reflector (TCR) as a polarimetric calibrator is presented in this letter. The transpolarizing TCR presents a high backscattered cross-polar response contrary to standard TCRs. The performance of this device has been tested at the X-band using the Universitat Politecnica de Catalunya ground-based synthetic aperture radar.

This paper presents a novel technique for automatic edge enhancement and detection in synthetic aperture radar (SAR) images. The characteristics of SAR images justify the importance of an edge enhancement step prior to edge detection. Therefore, this paper presents a robust and unsupervised edge enhancement algorithm based on a combination of wavelet coefficients at different scales. The performance of the method is first tested on simulated images. Then, in order to complete the automatic detection chain, among the different options for the decision stage, the use of geodesic active contour is proposed. The second part of this paper suggests the extraction of the coastline in SAR images as a particular case of edge detection. Hence, after highlighting its practical interest, the technique that is theoretically presented in the first part of this paper is applied to real scenarios. Finally, the chances of its operational capability are assessed.

A new multi-scale PolSAR data filtering technique, based on a Binary Partition Tree (BPT) representation of the data, is proposed. Different alternatives for the construction and the exploitation of the BPT for filtering and segmentation are presented.\nResults with simulated and experimental PolSAR data are presented to shown the capabilities of the BPT-filtering strategy to maintain both spatial details and the polarimetric information.

Ratio filters for speckle noise reduction in SAR imagery are recursive filters where the image structure is iteratively recovered from an initial oversmoothed image. We show that the MBPolSAR filter could be interpreted as a ratio filter applied to the off-diagonal terms of the covariance/coherency matix. From this observation, we propose a new polarimetric ratio filter allowing us to recover the image structure from all the terms of the covariance matrix. In addition, we briefly look at how the additive noise component could also be exploited for the image structure extraction. Filtering results on both simulated and real PolSAR images are shown.

The work detailed in this paper analyzes the topographic phase retrieval process on forested areas by means of Polarimetric Interferometric SAR data. On the basis of the Random Volume over Ground scattering model, an alternative implementation for the retrieval of the topographic phase, avoiding the bias introduced by the volumetric scattering components is presented.

The use of a transpolarizing surface placed on one side of a trihedral corner reflector (TCR) as polarimetric calibrator is presented in this paper. The transpolarizing-TCR presents a high back-scattered cross-polar response. This structure has been tested at 9.65 GHz (X-band) with the help of the UPC GB-SAR system.

Speckle suppression in PolSAR images is an important step for the extraction of meaningful information from PolSAR images, especially for homogeneous extended targets. It has been shown that insufficient noise filtering resulting in low equivalent number of look (ENL) values will increase bias on incoherent polarimetric parameters such as the Cloude-Pottier parameters. In addition, meaningful high-frequency information, such as edges and point targets must be preserved. Adaptive filters have been the most successful in reaching a good compromise between noise suppression and detail preservation. A large set of artificial PolSAR images, which ground truth are realizations of Markov random fields, has been generated. Performance metrics are focusing on speckle suppression (ENL), edge preservation, relative errors on polarimetric parameters and point target preservation.

Speckle for multidimensional SAR data may be modeled as the combination of multiplicative and additive noise sources. As demonstrated, the use of this noise model does not corrupt the estimation of physical information from PolInSAR data. The definition of a model based PolInSAR filter allows also the computation of relative errors for estimated heights of forested areas from PolInSAR data.

Doñana National Park wetlands, in southwest Spain, undergo yearly cycles of inundation and drying out. These cycles, together with great extensions of annual helophytes, make of Doñana a rapidly changing environment. 43 ASAR/Envisat images of Doñana in HH, VV and HV polarizations were acquired throughout the hydrological year 2006/07 with the aim to monitor in detail an entire flooding cycle. The images were ordered in the seven ASAR swaths to achieve high observation frequency. Backscattering temporal signatures of two main land cover types were obtained in the three polarization configurations and six ASAR swaths. Polarimetric behavior of the signatures is analyzed with the aid of extensive site data, such as a precise digital elevation model and continuous records of water level and meteorological parameters. Conclusions on the feasibility to discriminate emerged versus flooded land are derived for the different incidence angles, land cover types and phenological stages.

In this paper, the potentials of polarimetric coherence-optimization techniques for differential interferometric SAR (DInSAR) applications are examined. For this purpose, the cutting-edge approaches available in the literature are considered. First, synthetic PolSAR data simulating homogeneous distributed scatterers are employed to demonstrate the convergence of the optimized differential phase to the deformation phase information. Then, real X-band ground-based PolSAR acquisitions concerning an urban environment are analyzed. The relation between optimum coherences and corresponding optimum phase in terms of deformation on retrieval is carefully analyzed using two zero-baseline fully-polarimetric data sets. In the end, general conclusions about the advantages and drawbacks of the alternative maximization approaches are drawn.

This paper constitutes an example of analysis proving that new satellite borne full polSAR data is favorable for automatic ship detection purposes. In particular, this paper is based on a multiscale method for automatic ship enhancement based on the wavelet transform on single channel data and it proposes its extension to full polSAR images. Then, the enhancement of contrast of the ships with respect to the background sea reached with the method proposed is compared to that obtained by the classical polarization entropy.

The Remote Sensing Laboratory of the Universitat Politecnica de Catalunya carried out a one-year measuring campaign in the village of Sallent, northeastern Spain, using a polarimetric ground-based synthetic aperture radar (SAR) sensor. The objective was to study the subsidence phenomenon induced by the salt mining activity conducted in this area up to the middle of the last century. Zero-Baseline polarimetric SAR (PolSAR) data were gathered at X-band in nine different days, from June 2006 to March 2007. In this letter, the problem of extracting subsidence information from fully PolSAR acquisitions for the retrieval of high-quality deformation maps is addressed. After compensating for the atmospheric artifacts caused by troposphere changes, the linear component of the deformation process is estimated separately for each polarization channel with the Coherent Pixels Technique (CPT). Afterward, a novel polarimetric approach mixing the differential-phase information of each polarization channel is proposed. The results obtained in the two cases are quantitatively compared, and the advantages provided by the polarimetric acquisitions are finally stressed.

This paper presents a study on the origin of the dominating scattering mechanisms observed in polarimetric synthetic aperture radar (SAR) images of ships. The study has been made by using numerical simulations, which have been carried out with a radar cross section (RCS) prediction tool (GRaphical Electromagnetic COmputing) and a SAR simulator. Extensive series of simulations has been run for realistic 3-D geometrical models of ships with various sizes. Different radar parameters, aspect angles, and sea surface states have been considered in the scenario. Data analysis with coherent target decompositions has indicated characteristic polarimetric signatures for particular ships within a specific range of viewing angles. This happens at highly oblique incidences where the responses appear to be less sensitive to changes in the operating frequency and bearing angles. Under such conditions, ship scattering can be schematized by the distribution of a set of guide scatterers with high RCS. Their positions and polarimetric characteristics are quantitatively summarized in a new feature vector, which has been proposed to be the basis for classification algorithms. Key ideas about this vector are presented at the end of this paper, jointly with some examples related to three different ships. Recent publications have shown that they can be successfully cast within a new unsupervised vessel classification scheme.

This paper evaluates the potentialities of polarimetric ship scattering for basing classification methods that provide reasonable performance within cluttered scenes. Both simulated and airborne polarimetric synthetic aperture radar (SAR) images have been used to validate the conclusions of a previous phenomenological study. Numerical simulations have been carried out with GRECOSAR, a polarimetric interferometric SAR simulation tool that is able to process highly complex targets with a fast and accurate radar-cross-section prediction module. A representative set of scenarios has been defined, which includes various realistic ship models, sea states, and imaging geometries. In all of them, a two-scale sea surface model precisely accounting for sea-ship interaction and sea clutter has been added. The analysis of different images has shown that, with an adequate spatial resolution, ships may be characterized by a particular spatial arrangement and polarization state distribution of dominant scattering centers. This feature has allowed one to propose a new classification algorithm, which shows a promising behavior after various preliminary tests. In this paper, the performance of this technique is further evaluated with realistic clutter. The results show that robust classification is possible even in highly cluttered scenes if quad-pol imagery is available. On the contrary, in low clutter conditions, the usage of less restrictive solutions, like circular dual-pol schemes, is feasible and may still get an acceptable performance.

A new approach for speckle reduction in polarimetric synthetic aperture radar (PolSAR) images based on the turbo iterative principle is proposed. The turbo iterative algorithm shows high performances due to the propagation of the information between two complementary filters. One filter can boost up the results of the other by processing its residue image and retrieving valuable information in the noise subspace.

This paper introduces statistical tools for the analysis of POL-inSAR data acquired over natural environments. Maximum likelihood procedures are provided to test particular structures of POL-inSAR data presentations an to estimate relevant parameters. These particular configurations concern the stationarity of the separate POLSAR information between, the equality of the optimal POL-inSAR projection vectors and the presence of reflection symmetry.

PolInSAR data is a good example of a high dimensional SAR data. A blind filtering of these data does not make use of possible constraints imposed by the scattering process. In this paper it is shown that the scattering over forested areas induces some constrains on the data that can be fruitfully exploited to improve the final filtering, with que consequent improved of the quantitative estimation process.

This paper presents a new benchmark for techniques validation based on a multidimensional database of simulated data. By exploiting a SAR simulator of complex targets, series of numerical simulations may be run for specific sets of observation conditions and the results made public. Targets are in principle focused on urban structures, despite any other type of man-made targets may be considered. User interaction has allowed to fix the range of values for some design parameters according to the experience gained with real data. Multi-baseline polarimetric SAR interferometry and SAR tomography are the techniques for which this benchmark has been initially conceived, despite other research areas may also benefit, as multi-temporal or multi-frequency analysis. With the resulting amount of images, an adequate testing set can become available for multidimensional methods, which validation with real imagery is difficult.

A one-year subsidence monitoring activity has been carried out by the Remote Sensing Laboratory (RSLab) of the Universitat Politecnica de Catalunya (UPC) using an X-band ground-based SAR sensor. The project aimed at studying the subsidence phenomenon induced by salt mining extraction of the past years in the village of Sallent (Spain). The polarimetric capability of the system allowed to gather zero-baseline POL-SAR data in the single-pass mode in 9 different days, from June 2006 to April 2007. The estimation of the linear component of deformation process obtained using each polarization channel separately is here compared with the result of a new approach exploiting the full knowledge of the scattering matrix at once. For this purpose, a coherence-based approach is employed. The higher amount information carried by polarimetric data is finally pointed out both in terms of number of reliable pixels selected within the scene and higher quality of the retrieved subsidence map.

In this letter, a coherence-based technique for atmospheric artifact removal in ground-based (GB) zero-baseline synthetic aperture radar (SAR) acquisitions is proposed. For this purpose, polarimetric measurements acquired using the GB-SAR sensor developed at the Universitat Politecnica de Catalunya are employed. The heterogeneous environment of Collserola Park in the outskirts of Barcelona, Spain, was selected as the test area. Data sets were acquired at X-band during one week in June 2005. The effects of the atmosphere variations between successive zero-baseline SAR polarimetric acquisitions are treated here in detail. The need to compensate for the resulting phase-difference errors when retrieving interferometric information is put forward. A compensation technique is then proposed and evaluated using the control points placed inside the observed scene.

Entropy, alpha, and anisotropy (H/alpha/A) of the polarimetric target decomposition have been an effective and popular tool for polarimetric synthetic aperture radar (SAR) image analysis and for a geophysical parameter estimation. However, multilook processing can severely affect the values of these parameters. In this paper, a Monte Carlo simulation is used to evaluate and remove the bias generated by the multilook effect on these parameters for various media composed of grassland, forest, and urban returns. Due to insufficient averaging, entropy is underestimated, and anisotropy is overestimated. We also found that the bias in the alpha angle can be either underestimated or overestimated depending on scattering mechanisms. Based on simulation results, efficient bias removal procedures have been developed. In particular, the entropy bias can be precisely corrected, and the amount of correction is independent of the radar frequency and SAR systems. Data from L-band Advanced Land Observing Satellite/phased array type L-band SAR, German Aerospace Research Center (DLR)/enhanced SAR, Jet Propulsion Laboratory (JPL)/airborne SAR, and X-band polarimetric and interferometric SAR are used for demonstration in this paper.

In this paper, a new framework to filter speckle noise in polarimetric synthetic aperture radar (PolSAR) data is presented. The proposed filter, named the model-based PolSAR (MBPolSAR) filter, is based on exploiting the multiplicative–additive speckle noise model for multidimensional SAR data. The entries of the sample covariance matrix are processed according to this multiplicative–additive speckle noise model as a function of the complex correlation coefficient. Hence, the covariance matrix elements are processed differently. This filtering scheme improves the reduction of speckle noise and ameliorates the estimation of the polarimetric information. The filtering performances of the MBPolSAR approach are first tested quantitatively by means of simulated PolSAR data. In a second stage, they are evaluated by means of experimental SAR data acquired by the Deutsches Zentrum fÜr Luft-und Raumfahrt.

This paper deals with the validation of a previously developed texture model for SAR data as well as its associated parameter estimation algorithm. The mentioned model is named the Anisotropic Gaussian Kernel (AGK) model and allows the description of the possibly nonstationary and anisotropic behaviour of texture on heterogeneous areas of SAR images. The parameter estimation performance is evaluated over simulated data. We also investigate about the validity of our model over experimental data, by means of dissimilarity measures.

The identification of oil slicks on the ocean surface from SAR data requires quantitative sound models accounting for the most important characteristics (ocean spectrum, slick viscosity, slick shape, and so on). In this paper we present the implementation of an innovative SAR raw signal and image simulator, which is able to reproduce images relative to ocean surfaces covered by oil slicks with arbitrary shapes. The attention is mainly focused on slicks with fractal contours. The fractal Weierstrass-Mandelbrot function is used to generate slicks with fixed fractal dimension. A box counting technique is employed to evaluate the fractal dimensions of the generated slicks and the corresponding SAR images. Radiometric properties of the area covered by oil are also estimated in order to show how the simulated data provide a powerful set for processing algorithms.

Bistatic SAR systems are an emerging research field. In which context, the Universitat Politecnica de Catalunya is developing a ground based bistatic system using ESAs ENVISAT and ERS-2 as transmitters. In this paper we characterize the bistatic interferometric phase and the different sources of decorrelation. We will also present a bistatic interferometric simulator which is able to generate realistic synthetic bistatic interferograms. The simulator is validated with real data obtained with our bistatic acquisition system, named SABRINA.

A novel transpolarizing or crosspolarizing surface has been proposed to be applied in polarimetric SAR calibrating systems, like trihedrals, since they can not provide initially a crosspolar response. So the trans-surface has been designed and measured in an anechoic chamber, providing good results for normal incidence.

Entropy, alpha and anisotropy (H/α/A) of the polarimetric target decomposition has been an effective and popular tool for polarimetric SAR image analysis and geophysical parameter estimation. However, multi-look processing can severely affects the values of these parameters. In this paper, we evaluate the bias problem in H/a/A due to insufficient averaging. We found that the estimated bias is radar frequency dependent. A procedure for bias compensation is proposed. Data from L-band DLR/E-SAR and L-band JPL/AIRSAR, and X-band PI-SAR data are used for demonstration in this study.

This work presents an analytical study of the Quegan’s PolSAR data calibration algorithm. As it shall be demonstrated, the solution proposed by Quegan based on certain approximations, gives in certain situations, biased crosstalk ratios u, v, w and z. An improved Quegan’s calibration algorithm is proposed and tested.

A new approach to filter speckle noise in multidimensional SAR data, based on the multiplicative-additive speckle noise model, is presented. This approach is based on processing the elements of the sample covariance matrix differently, according to the complex correlation coefficient. As it is demonstrated with experimental PolSAR data, the filter does not produce a loss of information, but an improvement of the capabilities to filter speckle noise.

This paper presents a preliminary study about the scattering properties of urban-like scatters based on simulated SAR images. A simple target performed by a box of gypsum located over a perfectly conducting flat plane is analyzed for different views in both ISAR and SAR fully-polarimetric modes. The results are analyzed with the Pauli decomposition theorem and they show that the scattering response of such a target is dominated by a strong scatter, which polarimetric behavior depends on the relative orientation of the target with respect to the radar. Tests with interferometry shows that the height of the box can be reasonably retrieved despite of model simplicity.

In this work, a preliminary study on the use of polarimetric persistent scatterers for differential interferometric applications within an urban environmental is presented. The PolSAR measurements campaign that the RSLab of UPC is carrying on in the village of Salient using an X-Band ground- based SAR sensor is first described. The work is then focused on the additional information the knowledge of full scattering matrix [S] may provide with respect to the single polarization approach. The problem of instability of the polarimetric signature of urban targets is also analyzed. Finally, a solution based on the search of repeated patterns in the long-temporal profile of pixels' polarimetric signature is briefly introduced.

Thanks to their capability to cover large areas, in all weather conditions, during the day as well as during the night, spaceborne Synthetic Aperture Radar (SAR) techniques constitute an extremely promising alternative to traditional surveillance methods. Nevertheless, in order to assure further usability of SAR images, specific data mining tools are still to be developed to provide an efficient automatic interpretation of SAR data. The aim of this paper is to introduce texture analysis performed in the framework of time - frequency theory, as a means to detect oil spills in the sea surface. In particular, an algorithm permitting a precise quantitative characterization of the border between the oil spill candidate and the sea, will allow a novel classification of oil spills and look-alikes.

This paper deals with spatial analysis of texture in synthetic aperture radar (SAR) images. A new parametric model for local two-point statistics of the image is introduced, in order to characterize the spatially nonstationary and anisotropic behavior of the image. The texture is first modeled by a nonstationary Gaussian process resulting from the convolution of a Gaussian white noise with a field of anisotropic Gaussian kernel with spatially varying parameters. Hence, under the hypothesis of locally stationary signal, the analytic expression of the local autocovariance is derived. It is then explained how to simulate nonstationary K-distributed random fields by combining the new model with an already existing simulation method. A method for parameter estimation is then introduced. This method, based on the statistical product model, first corrects the speckle contribution to the local autocovariance and estimates the parameters of the model by analyzing the shape of the autocovariance. The algorithm is then evaluated over simulated and experimental data. Stationary simulations permit to show that, for a sufficient sample size, the estimator is unbiased. A test over a nonstationary simulation proves the ability of the algorithm to capture the spatial fluctuations of the texture. Finally, the method is applied to the experimental SAR data, and it is shown that a large amount of spatial information may be retrieved from the data.

Speckle noise represents one of the major problems when synthetic aperture radar (SAR) data are considered. Despite the fact that speckle is caused by the scattering process itself, it must be considered as a noise source due to the complexity of the scattering process. The presence of speckle makes data interpretation difficult, but it also affects the quantitative retrieval of physical parameters. In the case of one-dimensional SAR systems, speckle is completely determined by a multiplicative noise component. Nevertheless, for multidimensional SAR systems, speckle results from the combination of multiplicative and additive noise components. This model has been first developed for single-look data. The objective of this paper is to extend the single-look data model to define a multilook multidimensional speckle noise model. The asymptotic analysis of this extension, for a large number of averaged samples, is also considered to assess the model properties. Details and validation of the multilook multidimensional speckle noise model are provided both theoretically and by means of experimental SAR data acquired by the experimental synthetic aperture radar system, operated by the German Aerospace Center

Entropy, alpha and anisotropy (H/alpha/A) of the polarimetric target decomposition of Cloude and Pettier has been an effective and popular tool for polarimetric SAR image analysis and geophysical parameter estimation. However, multi-look processing can severely affect the values of these parameters. In this paper, a Monte Carlo method is used to evaluate the multi- look effect on these parameters for various media of grass, forest and urban. The effect of pixel correlation due to over sampling, and the mixed pixel effects will also be investigated. DLR/E-SAR and JPL/AIRSAR L-band data are used in this study.

The presence of speckle noise in Synthetic Aperture Radar images prevents a correct interpretation, as well as, the information retrieval processes. It has been recently demonstrated that speckle noise may introduce biases into the retrieved physical information when multidimensional data is considered. In case of multidimensional SAR systems, for single-look data, it has been proved that speckle noise is due to the combination of multiplicative and additive noise sources. This paper details the extension of this noise model to multilook, multidimensional SAR data.

In this paper, a study of the temporal evolution of the elements of 3 x 3 covariance matrix using an X-band polarimetric ground-based SAR sensor is proposed. Although the heterogeneity of the scenario allows to select different target typologies, the study is mainly focused on the analysis of azimuthally symmetric distributed targets. The fluctuations of the most representative elements of [C] as a function of time and the decorrelation among the polarimetric channels are investigated. A relation with atmospheric parameters like temperature, humidity and wind is considered in order to make out the weight these parameters can influence the polarimetric signature of the observed area.

After reviewing and discussing the difficulties of dealing with automatic interpretation methods in SAR imagery, the advantages of using a multiscale time-frequency framework will be established. Then, a specific technique for automatic spot detection, based on the Wavelet Transform (WT), will be presented and justified. The performance of the proposed algorithm will be tested, validated and compared with respect to other algorithms. The particular difficulties of automatic ship detection in near shore waters will be then briefly discussed and, aiming to increase ship detection rates in these regions, a novel automatic algorithm for the extraction of elongated structures such as the coastline will be presented and tested.

The detection of an isolated localized structure in a noisy background, with no a priori information about its presence, nor its shape presents multiple drawbacks. Nevertheless, this problem is present in a great number of applications as, for instance, ship detection from satellite radar imagery. Conventional algorithms for ship detection are conceived to discriminate an exceptionally bright localized pattern according to an established decision rule, expressed by the determination of a threshold. But the adjustment of thresholds involved in the decision step is complicated and relies on the existence of a considerable contrast between the vessels and the surrounding sea clutter which is not always reached. Thus, a novel method, based on the intrascale dependencies between wavelet coefficients in the different subbands, is proposed, justified and successfully tested on simulated and real images.

The aim of this paper is to present two novel polarimetric parameters, the eigenvalue relative difference (ERD) and the single bounce eigenvalue relative difference (SERD), to characterize natural media. These parameters are derived from the eigen-decomposition of the coherency matrix considering the reflection symmetry hypothesis. An analysis of these parameters is performed on multi-frequency polarimetric SAR data acquired on bare soils and forested areas.

The main advantage of multidimensional SAR data is the possibility to perform quantitative remote sensing in order to characterize the Earth surface. In most of the cases, the quantitative retrieval of physical parameters is performed on the basis of the correlation properties of the set of SAR images, measured by the correlation coefficients and coherence parameters. As demonstrated, coherence estimation is affected by several problems as the presence of biases or the loss of spatial resolution with respect to the original SAR images. The scope of this paper is to characterize accurately the coherence parameter, especially in the case of interferometric SAR data, by considering a new model for speckle noise in multidimensional SAR data proposed by the authors. Results with experimental data are presented.

In this paper, the first results of the Polarimetric Ground-Based SAR system developed at the Technical University of Catalonia (UPC) are presented. A heterogeneous scenario containing different kinds of targets such as low vegetation, forest and urban areas has been chosen for performing a measurement campaign. The monitoring activity has dealt with the observation of the test-site at X-Band for a whole day with a revisiting time of approximately one hour: a temporal sequence of PolDInSAR dataset has been gathered. A first study of temporal decorrelation effects on multi-polarization differential coherence concerning different kinds of targets is here shown. Preliminary results are commented and future activities are proposed.

Carrying out an effective monitoring and control of fishing activities is indispensable to guarantee a sustainable exploitation of sea resources. Nowadays, it is widely assumed that spaceborne synthetic aperture radar (SAR) constitutes a valuable and effective tool for this purpose. Nevertheless, automatic interpretation of SAR images is often puzzling. Hence this paper proposes a new framework for the understanding and handling of SAR data based on the wavelet transform. Specifically, a novel algorithm for ship detection is proposed, justified and tested. On the other hand, this paper aims at bringing some insight at ship detection capabilities of light polarimetric systems.

Carrying out an effective control of fishing activities is essential to guarantee a sustainable exploitation of sea resources. Nevertheless, as the regulated areas are extended, they are difficult and time consuming to monitor by means of traditional reconnaissance methods such as planes and patrol vessels. On the contrary, satellite-based synthetic aperture radar (SAR) provides a powerful surveillance capability allowing the observation of broad expanses, independently from weather effects and from the day and night cycle. Unfortunately, the automatic interpretation of SAR images is often complicated, even though undetected targets are sometimes visible by eye. Attending to these particular circumstances, a novel approach for ship detection is proposed based on the analysis of SAR images by means of the discrete wavelet transform. The exposed method takes advantage of the difference of statistical behavior among the ships and the surrounding sea, interpreting the information through the wavelet coefficients in order to provide a more reliable detection. The analysis of the detection performance over both simulated and real images confirms the robustness of the proposed algorithm.

A novel method to estimate interferometric coherence in synthetic aperture radar interferometry is proposed. It is demonstrated that this approach is not affected by the terrain topography, contrary to multilook techniques. In addition, since the method is based on the two-dimensional discrete wavelet packet transform, it allows recovering coherence information with a high spatial resolution. Results derived from simulated and experimental ESAR-DLR X- and L-band interferograms corroborate the performance of the proposed technique.

The performance of quantitative remote sensing based on multidimensional synthetic aperture radars (SARs), and polarimetric SAR systems in particular, depends strongly on a correct statistical characterization of the data, i.e., on a complete knowledge of the effects of the speckle noise. In this framework, the eigendecomposition of the covariance or coherency matrices and the associated H//spl alpha/_/A decomposition have demonstrated the potential for quantitative estimation of physical parameters. In this paper, we present a detailed study of the statistics associated with this decomposition. This analysis requires the introduction of mathematical tools that are not well known in the remote sensing community. For this reason, we include a review section to present them. Using this work, we then present an expression for the probability density function of the sample eigenvalues of the covariance or coherency matrix. The availability of this expression allows a complete study of the separated sample eigenvalues, as well as, the entropy H and the anisotropy A. As demonstrated, all these parameters must be considered as asymptotically nonbiased with respect to the number of looks. In order to reduce the biases for a small number of averaged samples, a novel estimator for the eigenvalues is proposed. The results of this work are analyzed by means of simulated and real airborne SAR data. This analysis permits us to determine in detail the effects of the number of averaged samples in the estimation of physical information in radar polarimetry.

This paper concerns the analytical study of the eigen decomposition of hermitian, positive semidefinite matrices applied to PolSAR data analysis. Based on the Gaussian scattering assumption for multidimensional SAR data, the joint distribution of the sample eigenvalues of the coherency, or covariance, matrices is derived for a general case. The distribution is particularized for PolSAR data, and the moments of the sample eigenvalues, the entropy (H), and the anisotropy (A) are analyzed

Carrying out an effective control of fishing activities is essential to guarantee a sustainable exploitation of sea resources. As the regulated areas are extended, satellite-based synthetic aperture radar (SAR) provides a powerful surveillance capability allowing the observation of broad expanses, independently from weather effects and from the day/night cycle. This paper proposes a novel approach for ship detection based on the analysis of oceanic SAR images by means of the wavelet transform. The analysis of the detection performance over both simulated and real RADARSAT SAR images confirms the robustness of the proposed method: ships, undetectable with other conventional techniques are noticeably sharpened, whereas background noise is drastically reduced

Speckle noise is known as one of the main problems in Synthetic Aperture Radar (SAR) technology. For multidi- mensional SAR data, a novel speckle noise model has been proposed. This paper presents the use of this model to reduce the effects of speckle noise. The performance of this new approach is demonstrated by means of simulated and real multidimensional SAR data. mation by acquiring two SAR images from slightly different positions, whereas PolSAR is able to recover complex scat- tering information by acquiring SAR images in an orthogonal polarization basis for the received and the transmitted waves. By combining these two approaches, PolInSAR is able to provide information concerning the vertical structure of the scatterer under study. Since all these approaches are based on the SAR system's coherence nature, multidimensional SAR data are also affected by speckle noise. Therefore, speckle noise reduction techniques are also necessary in these cases. A multidimensional speckle noise model cannot be obtained by an extension of the noise model of one-dimensional SAR images. There exist several ways in which multidimensional SAR imagery can be considered. One of these is the covariance matrix formulation that is constructed on the basis of the complex Hermitian product of a pair of SAR images. In this case, the authors have proposed a novel multidimensional speckle noise model (1). The speckle noise nature depends on the data's correlation structure, in such a way that speckle noise is dominated by an additive behavior for low coherence areas, whereas it is dominated by a multiplicative nature for high coherences. This paper presents a new approach to filter speckle noise in the case of multidimensional SAR imagery on the basis of the multidimensional speckle noise model.

Synthetic aperture radar (SAR) data are affected by speckle noise, originated by the SAR system's coherent nature. The problem of speckle noise in one-dimensional (1-D) data is already solved, as speckle has a multiplicative characteristic. SAR polarimetry represents an extension to multidimensional data by the use of polarization wave diversity. As a consequence of the existence of a correlation degree between the SAR images, the 1-D speckle noise model cannot be extended to multidimensional SAR data. This paper is devoted to present a completely new speckle noise model for the complex covariance matrix describing polarimetric SAR data in the distributed scatterers case. As is shown, this new model is able to identify which are the noise mechanisms in all the covariance matrix elements. The speckle noise model is validated by using real L-band polarimetric data acquired with the German E-SAR sensor.

In this paper, a noise model for the extended six dimensions covariance matrix used in polarimetric synthetic aperture radar (SAR) interferometry is proposed. This noise model is able to interpret interferometric, as well as polarimetric noise characteristics for the diagonal and non-diagonal elements of the covariance matrix.

This paper addresses the problem of interferometric phase noise reduction in synthetic aperture radar interferometry. A new phase noise model in the complex domain is introduced and validated by using both simulated and real interferograms. This noise model is also derived in the complex wavelet domain, where a novel noise reduction algorithm, which is not based on a windowing process and without the necessity of phase unwrapping, is addressed. The use of the wavelet transform allows to maintain the spatial resolution in the filtered phase image and prevents to filter low coherence areas. By using both, simulated as well as real interferometric phase images, the performance of this algorithm, in terms of noise reduction, spatial resolution maintenance, and computational efficiency, is reported and compared with other conventional filtering approaches.