Synthetic aperture radar (SAR) data classification has gained significant research interest, as accurate land-cover information is vital in a wide range of planning and management activities. While classification algorithms for full-polarimetric (full-pol) SAR data are typically based on the statistical or physical characteristics of the scattering mechanism from targets, classification of co-cross polarization (VV-VH or HH-HV) dual-polarimetric (dual-pol) SAR data has traditionally relied on backscatter intensity information due to its limited polarimetric information. Several studies also employ the dual-pol entropy/alpha decomposition parameters, establishing a conventional framework for supervised and unsupervised classification of dual-pol SAR data. However, it is essential to note that the conventional approach cannot differentiate between certain elementary targets, leading to misclassification among diverse land-cover targets. To address this limitation, we introduce an unsupervised clustering technique for dual-pol Sentinel-1 SAR data utilizing the conventional entropy parameter alongside a dual-pol target characteristic parameter that discriminates between various land-cover targets, including “dihedral-like” (buildings, etc.) and “surface-like” (water bodies, etc.) targets in a dual-pol scene. Thus, the proposed clustering scheme, which applies to both single look complex (SLC) and ground range detected (GRD) SAR, categorizes it into eight clusters, each representing specific target characteristics. We adopted two strategies to assess the proposed clustering scheme: 1) cluster zones obtained for diverse land-cover targets spanning continents and 2) temporal changes in cluster zones over rice-cultivated fields at various growth stages. The proposed approach effectively discriminates diverse land-cover targets and distinct growth stages of rice.

This paper explores four distinct target descriptors derived from full-polarimetric Synthetic Aperture Radar (SAR) data. Initially, we define a 2 × 1 real positive target vector by leveraging the mean and standard deviation of complex eigenvalues extracted from the 2 × 2 Sinclair matrix. This vector is the basis for two innovative parameters: 1) the scattering-type parameter, and 2) the scattering asymmetry parameter. Furthermore, we introduce the scattering purity and complexity parameters derived from the mean and standard deviation of the real positive eigenvalues of a Hermitian positive semi-definite 3 × 3 coherency (covariance) matrix. We highlight the efficacy of these parameters by conducting an experimental analysis with several canonical targets. Subsequently, we investigate their performance by thoroughly examining Radarsat-2 full-polarimetric SAR data.

In flood management practices, the identification of areas inundated by flood water is vital in mitigating future events. SAR data can be used to map and extract flood extent. The use of Polarimetric SAR in change detection between datasets acquired before and during floods can be used for rapid and accurate flood mapping. Polarimetric data improves the accuracy of the detection of floods. Four change detection methods (Optimization of Power Difference (OPDiff), Power Ratio (OpRatio), difference in Cloude-Potier eigenvalues and difference in backscatter intensity) were used in change detection for flood event in Imola, Italy. Dual-Pol Sentinel-1 data was analysed and the accuracy of the detection was evaluated against PlanetScope optical data. The results show that difference in Cloude-Potier second eigenvalue had the highest accuracy which was identical to difference in VH data.

Peatlands are important carbon sinks however many peatlands have been degraded. This lowers the water level releasing greenhouse gases into the atmosphere. Synthetic aperture radar (SAR) satellite data can be used to monitor the water level in peatlands and therefore identifies areas that are in need of restoration. Using quad-pol L-band data from ALOS-2 shows potential to identify negative change in the water level. Observing eigenvalues and eigenvectors of the change matrix of a bog in Scotland identifies a lower water table (drier peatland) that corresponds with less surface scattering. In addition, observing the coherence regions of different points around peatlands shows variations in the scattering. Improvements to these results would be made by obtaining acquisitions on dates which had a higher water table.

Directly remote sensing plastic pollution within the open ocean has proven to be challenging. Therefore, other methods of detecting marine plastics through a proxy should be explored. Radar satellites are sensitive to surface roughness and can therefore detect dampening of wind-driven capillary waves on the sea surface. Plastics within the ocean have been found to be colonized by microbes, which can produce surfactants, substances which can dampen the short capillary waves on the ocean. This research investigates whether a ground radar instrument is capable of detecting plastisphere-based surfactant dampening within a semi-natural environment setting. We find that we can observe reduced backscatter from dampening effects that are occurring within our experiment setting. We also find that the backscattering from experiments involving plastisphere-based surfactants are significantly different from control experiments where microbial production is stopped.

Characterizing targets with dual-polarimetric (dual-pol) Synthetic Aperture Radar (SAR) data has traditionally relied only on backscatter intensity. However, the limitations of conventional dual-pol parameters, such as the inability to differentiate between orthogonal targets like dihedral and trihedral structures, result in misclassification among diverse targets. This study proposes an innovative target characteristic parameter derived from both dual-pol single-look complex (SLC) and ground range detected (GRD) SAR data that effectively distinguishes between "dihedral-like" and "surface-like" targets, enabling improved characterization of diverse land cover targets.

Accurately estimating forest component above-ground biomass is crucial for understanding and analyzing the growth process of vegetation ecosystems, interpreting the ecosystem carbon cycle correctly, and improving the effectiveness of forest management. It also provides ideas for the bottleneck problem of low saturation point in forest aboveground biomass (AGB) estimation using remote sensing techniques. This paper extracts a large number of remote sensing features from optical GF-1 and GF-3 SAR data. A feature optimization inversion model named KNN-FIFS (a fast iterative procedure embedded in K-nearest neighbor) was applied here for component and total forest AGB inversion. Four forest types including Yunnan pine, Simao pine, broadleaf forest, and mixed conifer and broadleaf forest were involved in this study. The results indicate that the summation of the estimated each component AGB for total forest AGB estimation showed higher inversion accuracy than the total AGB estimated directly by remote sensing features. The relative RMSE difference between them was about 3%. The results also revealed that through forest component estimation for later total forest AGB estimation can help improve the saturation points in forest AGB estimation using remote sensing datasets.

In this paper, we took GF-2 and GF-3 as datasets, introduced an image fusion method named HIS(Intensity-Hue-Saturation)-NSST (Nonsubsampled Shearlet Transform) +PCNN (Pulse Coupled Neural Network) method to fuse the different band combination of the GF-2 and 7 features extracted from GF-3 images, respectively. Then we use a fast iterative procedure embedded in K-nearest neighbor (KNN-FIFS) to estimate the forest AGB by the features extracted from original GF-2, GF-3, combination of GF-2 and GF-3, and fused images of GF-2 and GF-3. Their performance on forest AGB estimation were analyzed and compared.

This study proposes a target characterization technique using a complex scattering trace correlation measure. Several orthonormal projections for characterizing scattering information have been presented in the literature. For instance, one can project a particular target on different polarization bases. In doing so, two possible phenomena can happen: 1) either the structure of the target scattering vector entirely changes from one basis to another or 2) the vector retains some of its inherent characteristics while some get altered. Therefore, the complex correlation between the two projections provides information about the typology of the target present in the scene. Hence, in this study, we derive the amplitude and phase of the complex correlation measure between the linear and circular bases to describe different land cover targets. We have analyzed the proposed technique using the GaoFen-3 single look complex (SLC) image over San Francisco Bay, USA, the RADARSAT-2 SLC image over Vijayawada, India, and the ALOS PALSAR image over the coast of Futtsu, Japan. The results of these tests exhibit significant potential for retrieving comprehensive physical characteristics of the targets within the observed scenes.

Target characterization parameters are pivotal in accurately identifying and assessing diverse land cover targets in radar polarimetry. While full-polarimetric (full-pol) synthetic aperture radar (SAR) data offer numerous parameters, characterizing targets with HH-HV or VV-VH dual-polarimetric (dual-pol) SAR data has traditionally relied on backscatter intensity alone due to limited polarimetric information, which leads to ambiguities in characterizing diverse land cover targets. In response to this limitation, this study introduces a novel target characteristic parameter$\overline {\alpha }_{(k)}$derived from dual-pol single-look complex (SLC) and ground range detected (GRD) SAR data that are capable of discriminating between “dihedral-like” (buildings, bridges, ships, and so on) and “surface-like” (water bodies, bare fields, runways, and so on) targets, by employing a data-driven approach. We first derive a set of normalized descriptors independently of SLC and GRD SAR data to formulate two indices that characterize “dihedral-like” and “surface-like” targets. Using the two indices, we derive the dual-pol target characteristic parameter, providing a novel perspective on the intricate nature of radar responses from diverse land cover targets acquired by dual-pol SAR sensors. Furthermore, we employ this parameter to extract three scattering power components: “dihedral-like” ($P_{d-l}$), unpolarized ($P_{u}$), and “surface-like” ($P_{s-l}$) from both dual-pol SLC and GRD SAR data. We assess the proposed target characteristic parameter and scattering power components using Sentinel-1 images acquired over diverse land cover targets spanning six continents. This novel approach enables improved global land cover characterization with operational SAR missions such as Sentinel-1 and upcoming NASA-ISRO SAR (NISAR) missions.

Several orthonormal projections onto various bases have been proposed to analyze Polarimetric Synthetic Aperture Radar (PolSAR) data. These individual projections frequently lead to several additional ambiguities for target scattering characterization. Therefore, considerable confusion is common when using unsupervised classification approaches to classify targets. In this study, we project the scattering information onto several realizations of the normalized scattering configuration without imposing an orthogonality requirement. Using the full-polarimetric AIRSAR data over San Francisco, USA, we first compute the spectrum of the scattering-type parameter, θFP, and subsequently use it to categorize various land-cover targets.

Water hyacinth (WH) is one of the dreadful noxious invasive species whose origin is traced to South America. Compared to in-situ measurements, remote sensing offers a less expensive way of monitoring WH. In this research, the Optimization of Power Difference (OPDiff) change detection technique was used in mapping WH. Sentinel-1 IW SLC data from 2017 to 2022 were analyzed to get polarimetric covariance matrices using the VV, VH and VV*VH. Pre-processing, co-registration and detection steps were carried out using the GPT processor of ESA SNAP graph builder. The OPDiff applied in the study areas enabled the detection and extraction of spatial extent of WH in single image and in time series by identifying the changes occurring on the minimum eigenvalues. When evaluated against Sentinel-2 data, the algorithm recorded on average 92.6% & 89.9% precision and accuracy respectively. Heatmaps were generated for the study area and demonstrated variability of WH in time and space. This research demonstrates the capability of using polarimetric radar data to detect and monitor WH.

This study is to analyze the multi-polarization backscattering from intertidal areas using polarimetric synthetic aperture radar imagery collected by the X-band Cosmo-SkyMed Second Generation satellite mission. In this study, we focus on the Solway Firth (Scotland) area that includes different habitats as wetland, salt marshes, sand dunes, and mudflats. In particular, mudflats presents a large variability of normalized radar cross section that may limit significantly the classification performance of intensity-based approaches. Within this context, for the first time, we exploit full-polarimetric Cosmo-SkyMed Second Generation imagery collected in StripMap mode.

A glacial lake outburst flood (GLOF) is typically a natural phenomenon caused by rapid discharge of water from a glacier, leading to a flood. The frequency of GLOFs has increased significantly in the northern areas of Pakistan, which demands identification and continuous monitoring of potentially dangerous glacial lakes. In this paper, an up-to-date inventory of glacial lakes in this region is presented. This inventory (HKH-PK-2020) has been prepared using high resolution PlanetScope imagery acquired in 2020 over northern Pakistan. It contains a total of 8808 lakes. We compare our database with the High Mountain Asia (HMA) glacial lakes inventory over northern Pakistan, prepared in 2018 using Landsat imagery. The new inventory contains 6537 more glacial lakes than the HMA inventory. Furthermore, we have prepared an annotated dataset containing 3525 images (of high resolution PlanetScope imagery over a selected number of lakes from the inventory). Each image comprises 4 bands, namely red, green, blue, and near infrared. The annotations are binary: lake or background. Finally, we have performed an ablation study with two encoder-decoder based convolutional neural networks (CNNs) trained on this dataset for pixel-based classification. Our results show an intersection over union (IoU) score of 72.81% for the lake class, which is a promising first result indicating a use of deep learning for automated inventory updates in future.

The remote sensing of marine plastics is a relatively new field and research into radars capabilities for monitoring / detection is mostly limited. Here, we utilize the use of a C- and X-band ground radar to understand the capabilities for monitoring marine plastic pollution. Results show that backscattering differences can be found between reference acquisitions of clean water and test water filled with plastics, in both C- and X-band frequencies. With X-band detecting significant differences in backscattering in 48/68 test cases, and C-band detecting differences in 20/67 test cases.

With remote sensing increasingly used to support peatland management, this study evaluates the potential of quad-polarimetric L-band SAR for water table depth (WTD) monitoring. We produced Random Forest models predicting WTD, and temporal WTD variation, across multiple locations and dates at a Scottish raised bog, based on observables derived from quad-pol L-band SAR data. Like other studies, our models showed limited ability (R2= 0.05) to predict WTD across multiple locations. However, significant validated performance (R2= 0.66) was found when predicting temporal WTD variation, exceeding the performance found by comparable studies using C-band SAR. Therefore, further work using quad-pol L-band SAR is recommended, particularly to understand and correct for the effects of non-WTD characteristics on SAR signal which limit model performance spatially.

Forest biomass plays an essential role in forest carbon reservoir studies, biodiversity protection, forest management, and climate change mitigation actions. Currently, polarisation information shows great potential for reducing saturation problems and improving estimation accuracy. 137 SAR features including backscatter coefficients, texture characteristics and features extracted from H/A/a decomposition and so on 9 decomposition methods were extracted for L-band airborne PolSAR data at two test sites, respectively for forest L-band scattering mechanisms analysis and AGB estimation. A multiple linear stepwise regression (MSLR) model and a fast iterative feature selection for K-nearest neighbor (KNN-FIFS) method are used to estimate the forest AGB at the two test sites. In the present study, there was evident site dependence of the L-band forest scattering mechanisms, while KNN-FIFS performed better in the estimation of forest AGB. The best AGB estimation was acquired at the Hainan test site with RMSE = 28.88 t/ha and rRMSE = 18.46%.

A challenging issue in the field of marine remote sensing is the application of polarimetric synthetic aperture radar (PolSAR) to small ship detection in complicated environments. Several outstanding polarimetric detectors (such as the optimal polarimetric detector, polarimetric whitening filter, and polarimetric notch filter, etc.), have been effectively implemented in practical applications. A linear combination model based on quadratic optimization is summarized to establish a general framework for polarimetric detectors, transitioning the PolSAR ship target detection from a model driven approach to a hybrid (model/data)-driven approach. However, the dimension of the covariance matrix may be high, and the computation cost will be large. The higher dimension of the covariance matrix requires a bigger the data demand. As a result, when the sample size is small, the model performance will degrade. In this paper, to decrease the computational complexity and improve the robustness, we propose a novel method called the simultaneous diagonalization transform (SDT). The proposed method enables an almost simplest representation of information from the covariance matrix providing a rapid detection algorithm. The simulation experiments demonstrate that polarimetric detectors based on SDT consistently outperform those based on other methods in terms of accuracy, efficiency, and sample size requirements across various complex backgrounds. Furthermore, the effectiveness, robust, and fastness of the polarimetric detector based on SDT is validated using real data collected by RadarSAT-2, GaoFen-3, and Sentinel-1A.

Polarimetric Synthetic Aperture Radar (PolSAR) has the capability to improve performance in many remote sensing applications compared to the use of a single polarisation channel. The scattering matrix allows to analyse the polarimetric information of single targets. When dealing with distributed targets, speckle introduces a statistical variation on the visible polarimetric behaviour. Generally the adopted solution is to extract second order statistics building a covariance matrix. The covariance matrix formation as an averaged outer product of scattering vectors by themselves impose constraints on the power distribution in the polarimetric space (this is the shape of the surface drawn by the quadratic forms). The surface is forced to be an ellipsoid. In this work we show how such forcing can produces a loss of information when the target is not homogeneous or there are more than three orthogonal targets. We propose an alternative way to decompose the partial target into a sum of low entropy components which does not require indiscriminate pre-averaging. In previous work, we demonstrate its usefulness using Monte Carlo simulations and 1 real RADARSAT-2 quad-pol data [1]. In this work, we extended the analysis introducing 4 more RADARSAT-2 images and 7 ALOS-2 quad-pol data. We considered several test applications including coastal, icebergs, urban, agricultural. Comparing the results of the proposed decomposition with the ones obtained by the Cloude-Pottier decomposition and show how the use of indiscriminate averaging can result in an apparent increase of entropy of the polarimetric information, with consequent loss of part of the information.

Plastics in the riverine environment are of major concern due to their potential pathways into the wildlife and more generally the ocean. Dams are capable of trapping marine plastics within the riverine environment. This entrapment changes the surface roughness of the area with the marine debris. Radar satellites are sensitive to surface roughness and can therefore detect these changes. This research investigates areas in Serbia and Boznia & Herzegovina using ESA Sentinel-1 polarimetric SAR data. This study shows the feasibility of detecting large accumulations of plastic near dams, with detectors capable of achieving 75-85% positive detection ratings with a 0.1% false alarm rate. Additionally, we find the use of single VV polarization is inadequate for this task and PolSAR data are needed.

Ship detection using polarimetric synthetic radar (PolSAR) imagery attracts a lot of attention in recent years. Most notably, the detector polarimetric notch filter (PNF) has been demonstrated to be effective for ship detection in PolSAR imagery, which gives excellent performances. In this work, a mathematical form of one new PNF (NPNF) based on physical mechanisms of targets and clutter is further developed for partial targets. The different mechanisms have been revealed based on the projection matrix. The experimental results including simulated and measured data demonstrate that the NPNF exhibits a better performance than the original PNF.

Ship detection via synthetic aperture radar (SAR) has been demonstrated to be very useful as polarimetric information helps discriminate between targets and sea clutter. Among the available polarimetric detectors, optimal polarimetric detection (OPD) theoretically provides the best detection performance under the assumption that the fully developed speckle hypothesis stands. This study proposes a polarimetric detection optimization filter (PDOF). The target clutter ratio (TCR) over the speckle variation was maximized using a matrix transform to derive the PDOF. The objective function based on a matrix transform instead of a vector transform is optimized to obtain synthetic effects by combining a polarimetric whitening filter (PWF) and a polarimetric matched filter (PMF). Subspace form of the PDOF (SPDOF) is also proposed, which gives performance comparable to the PDOF. Assuming a Wishart distribution, the exact and approximate expressions of the closed-form probability density function (PDF) of the PDOF are derived. The probability of false alarm (PFA) was derived in a closed-form expression, which allows obtaining the PDOF threshold analytically. Moreover, the gamma model is extended to a generalized gamma distribution ($\text{G}\Gamma \text{D}$) to adapt complicated resolutions and sea states. Experiments with simulated and real data validate the correctness and effectiveness of the results. The PDOF detector achieves the best performance in most virtual and real-world environments, especially in cases where the target statistics and clutter are not Wishart-distributed.

Ship detection is an important task in civil or military applications and we can use polarimetric synthetic aperture radar (PolSAR). Many polarimetric detectors were proposed and achieved good performances in particular environments, such as optimal polarimetric detector (OPD), polarimetric whitening filter (PWF), polarimetric notch filter (PNF), polarimetric detection optimization filter (PDOF) and diagonal loading detector (DLD) etc. Up to know, the analytical links among different polarimetric detectors have not been found. In this work, the above polarimetric detectors are unified in mathematical forms and a general framework of polarimetric detectors based on quadratic optimization is presented. The mathematical forms are summarized as a trace of two matrices’ product. One is a detection transformation matrix and the other is the polarimetric covariance matrix of the pixel to be detected. We find that all these polarimetric detectors can be regarded as the optimization of such detection matrix, which is the key point of the general framework, and the difficulty turns to be a linear inseparable problem. Pocket Perceptron Linear Algorithm (PPLA) is used to solve the linear inseparable problem. In the case of low resolution, target detection is almost an indivisible problem, and multilayer perceptron (MLP) cannot provide better detection results than PPLA. In the case of high resolution, target detection becomes a nonlinear separable problem, and MLP is gradually superior to PPLA. Additionally, the optimal weights of the recent DLD are obtained to compare with other detectors in the general framework and the DLD is developed to a more general case (GDLD). The experiments validate the general framework of polarimetric detectors. Different detectors in the general framework are utilized and compared in both simulated and measured PolSAR data. The results show the optimal solution in the general framework can always reach the best performance, and the GDLD is the closest one to the optimal detector of the general framework.

Polarimetric synthetic aperture radar (PolSAR) is widely used in remote sensing and has important applications in the detection of ships. Although many polarimetric detectors have been proposed, they are not well combined. Recently, a polarimetric detection optimization filter (PDOF) was proposed, which performs well in most environments. In this study, a novel subspace form of the PDOF [strict PDOF (SPDOF)] was further developed based on the Cauchy inequality and matrix decomposition theories, enhancing detection performance. Furthermore, a simple method to determine the optimal dimension of the subspace detector based on the trace ratio form was proposed by calculating the area under the receiver operating characteristic (ROC) curve, reaching the best detection performance among the subspaces of the detector. Moreover, to combine different subspace detectors, a modified linear discriminant analysis was proposed and developed for the diagonal loading detector (DLD) based on polarimetric subspaces. The experimental results demonstrate the superiority of these joint polarimetric subspace detectors. Most importantly, DLD solves for previous limitations due to the complex clutter background and achieves a performance comparable to that of the Wishart (Gaussian) distribution, particularly in the low target-to-clutter ratio (TCR) case.

Synthetic aperture radar (SAR) polarimetry can improve change detection in terms of detection capabilities. In this work, we are proposing to extend the idea of target decomposition to changes affecting partial targets. This will allow the separation of polarimetric-dependent changes, providing extra information that can be used to better understand the processes affecting the targets. Three models for changes are proposed and compared. The methodologies are based on Lagrangian optimizations of distinct operators built using quadratic forms for a power ratio and a power difference. The optimizations can be accomplished by diagonalizations of specific matrices derived from polarimetric covariance matrices. These are, therefore, spectral decompositions of an appropriate matrix which we define as change matrix. The theoretical validity of the models is assessed using Monte Carlo simulations. Additionally, we perform real data validation exploiting L-band quad-polarimetric data from the E-SAR (DLR) SARTOM 2006 campaign and ALOS PALSAR (JAXA) acquisitions in Morecombe Bay (U.K.). We observed that the two algorithms based on power difference allow to decompose the change into the minimal set of scattering mechanisms (SMs) that have been added or removed from the scene. The two algorithms differ on the initial assumption on the change. On the other hand, the ratio operator provides a better detection performance although the eigenvalues do not correspond to meaningful SMs. A combination of the three methodologies can, therefore, improve detection and classification of changes.

This article investigates, for the first time, the inclusion of the operator Trace Coherence (TrCoh) in polarimetric and interferometric synthetic aperture radar (SAR) methodologies for the estimation of biophysical parameters of vegetation. A modified inversion algorithm based on the well-known Random Volume over Ground (RVoG) model, which employs the TrCoh, is described and evaluated. In this regard, a different set of coherence extrema is used as input for the retrieval stage. In addition, the proposed methodology improves the inversion algorithm by employing analytical solutions rather than approximations. Validation is carried out exploiting single-pass HH-VV bistatic TanDEM-X data, together with reference data acquired over a paddy rice area in Spain. The added value of the TrCoh and the convenience of the use of analytical solutions are assessed by comparing with the conventional polarimetric SAR interferometry (PolInSAR) algorithm. Results demonstrate that the modified proposed methodology is computationally more effective than current methods on this dataset. For the same scene, the steps required for inversion are computed in 6 min with the conventional method, while it only takes 6 s with the proposed approach. Moreover, vegetation height estimates exhibit a higher accuracy with the proposed method in all fields under evaluation. The root-mean-squared error reached with the modified method improves by 7 cm with respect to the conventional algorithm.

The main objective of this paper to show the potential of multitemporal Sentinel-1 (S-1) and Sentinel-2 (S-2) for detection of water hyacinth in Indian wetlands. Water hyacinth (Pontederia crassipes, also called Eichhornia crassipes) is one of the most destructive invasive weed species in many lakes and river systems worldwide, causing significant adverse economic and ecological impacts. We use the expectation maximization (EM) as a benchmark machine learning algorithm and compare its results with three supervised machine learning classifiers, Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbour (kNN), using both synthetic aperture radar (SAR) and optical data to distinguish between clean and infested waters.

Iceberg properties, together with meteorological and environmental conditions can influence Synthetic Aperture Radar (SAR) backscatter behaviours. In this work, we used five images of quad-pol ALOS-2/PALSAR-2 SAR data to analyse icebergs in Greenland. We investigate the scattering mechanisms through several observables and decompositions. Our results show that the most common scattering mechanisms for icebergs is surface scattering and volume scattering. Sometimes double bounce is also observed. By performing a multi-scale analysis using boxcar$5 \times 5$and$11 \times 11$window sizes, we conclude that icebergs can be a collection of strong scatterers. This gives hope for using quad-pol polarimetry to provide some iceberg classifications in the future.

Icebergs represent hazards to ships and maritime activities and therefore their detection is essential. Synthetic Aperture Radar (SAR) satellites are very useful for this, due to their capability to acquire under cloud cover and during polar nights. Additionally, polarimetry has been proven to improve the detection capability. In this work, we compare six state-of-the-art quad polarimetric detectors to test their performance and ability to detect small sized icebergs in four locations in Greenland. These were the polarimetric notch filter (PNF), polarimetric match filter (PMF), polarimetric whitening filter (PWF), optimal polarimetric detector (OPD), reflection symmetry detector, and the dual polarisation anomaly detector (iDPolRAD). We use four single look complex ALOS-2 quad pol images. The data were calibrated and processed. We produce the covariance matrices of each image before applying a testing and training window for detection. We also add a guard window to reduce false alarms. Our results show that the multi-look polarimetric whitening filter and optimal polarimetric detector provide the most optimal performance in quad and dual pol mode detection.

Wetlands are among the most productive natural ecosystems in the world, generally being important biodiversity hotspots. However, the complex nature of these landscapes together with the fragile and dynamic relationships among the organisms inhabiting these regions, make wetland ecosystems especially vulnerable to environmental disturbance, such as climate change. Thus, developing new automated systems which allow continuous monitoring and mapping of wetland dynamics is crucial for preserving their natural health. Synthetic aperture radar (SAR) systems have proven useful in monitoring and mapping the hydrological processes of wetland ecosystems through the use of polarimetric change detection techniques. Nonetheless, most of these flood change detectors rely on static detection approaches, generally covering a limited period of time (e.g., pre and post flooding scenario comparison), thus failing in providing continuous information about the diverse hydrological mechanisms. In this context, this research presents a novel approach for monitoring the hydrological dynamics of wetlands in a continuous and near-real-time manner using dense Sentinel-1 image time-series. In this work, we have enhanced our recently developed algorithm based on cumulative sums (SAR-CUSUM), to include polarimetric information, which allows to classify the type of change due to the flood. The new processing stack exploits the polarimetric information of dual-pol Sentinel-1 dense time series for detecting floods and provide some separation between open water and flooded vegetation areas. The outcomes derived from this study emphasize the capabilities of dense SAR time-series for environmental monitoring while providing a useful tool which could be integrated into rapid response and wetland conservation management plans.

Plastics within the ocean have been found to be colonised by microorganisms that, as a by-product of their metabolism, produce surfactants. Short capillary waves on the sea surface can get dampened due to the increased surface elasticity of these surfactants. Radar satellites are sensitive to surface roughness and can therefore detect the dampening of these waves. This research investigates areas inside the Atlantic, Pacific and Indian Ocean gyres using ESA Sentinel-1 and DLR TerraSAR-X data. We found out that we can observe several surfactant instances in the gyres and these are not correlated to medium or high level of chlorophyll. We can exclude that they have origin in biogenic slicks. Among other possible unknown origins, we hypothesise that these surfactants are produced from plastic concentrations within the ocean.

The detection of small ships in polarimetric synthetic aperture radar (PolSAR) images is still a topic for further investigation. Recently, patch detection techniques, such as superpixel-level detection, have stimulated wide interest because they can use the information contained in similarities among neighboring pixels. In this article, we propose a novel neighborhood polarimetric covariance matrix (NPCM) to detect the small ships in PolSAR images, leading to a significant improvement in the separability between ship targets and sea clutter. The NPCM utilizes the spatial correlation between neighborhood pixels and maps the representation for a given pixel into a high-dimensional covariance matrix by embedding spatial and polarization information. Using the NPCM formalism, we apply a standard whitening filter, similar to the polarimetric whitening filter (PWF). We show how the inclusion of neighborhood information improves the performance compared with the traditional polarimetric covariance matrix. However, this is at the expense of a higher computation cost. The theory is validated via the simulated and measured data under different sea states and using different radar platforms.

In this study, the capability of polarimetric Synthetic Aperture Radar (SAR) measurements to monitor harsh coastal areas is addressed. A meaningful showcase is presented that refers to the Scottish Solway Firth (SF) wetlands, an important coastal ecosystem severely affected by extreme weather conditions. Experiments are undertaken on a pair of full-polarimetric (FP) C-band RadarSAT-2 SAR images collected over SF during July and November 2018 in order to both extract the coastal profile and to identify changes in the scattering behavior along the coastal strip. The results demonstrate the soundness of the proposed approach and the role played by FP SAR measurements to effectively support vulnerability and risk assessments in harsh coastal environments.

This manuscript presents the analysis and a methodology for monitoring asparagus crops from remote sensing observations in a tropical region, where the meteorological conditions change considerably between production cycles. We use data provided by the Sentinel-1 satellite and temperature from a ground station to show how particularly the VH polarisation can be used for crop monitoring in order to visualise the canopy formation, the growth rate and canopy biomass, revealing high dependencies on temperature. We also present a multi-output machine learning regression algorithm trained on a rich spatio-temporal dataset in which each output estimates the number of asparagus stems that are present in each of the pre-defined crop phenological stages. We present the results of two separate scenarios: Using a single SAR image plus temperature as input for the algorithm and using multitemporal SAR data. Results show that the methodology presented is able to retrieve each individual monitored variable when using temperature as predictor with coefficients of determination (R2) above 0.85. Further research is currently investigating the added value of multitemporal SAR data to complement the predictions and potentially replace the temperature feature.

This work presents a novel methodology to extract and analyse multi-temporal polarimetric SAR (PolSAR) information from a stack of co-registered images. The method is based on the analysis of PolSAR changes between every image with respect to the rest of images in the stack. The changes are organized in a matrix form to encode the polarimetric evolution of a target. The change matrix is then used to visually understand a target evolution and its SAR response based on the evolution of scattering mechanisms due to the target physical evolution. Additionally, we design and test an image classification algorithm in a supervised learning fashion by using typical change matrices as training data. The methodology is tested exploiting C-band quad-pol RADARSAT-2 data with special interest on agricultural fields such as rice in Seville, South-West of Spain and in the Indian Head in Canada as part of the Agrisar 2009 campaign.

Constant false alarm rate (CFAR) algorithms using a local training window are widely used for ship detection with synthetic aperture radar (SAR) imagery. However, when the density of the targets is high, such as in busy shipping lines and crowded harbors, the background statistics may be contaminated by the presence of nearby targets in the training window. Recently, a robust CFAR detector based on truncated statistics (TS) was proposed. However, the truncation of data in the format of polarimetric covariance matrices is much more complicated with respect to the truncation of intensity (single polarization) data. In this article, a polarimetric whitening filter TS CFAR (PWF-TS-CFAR) is proposed to estimate the background parameters accurately in the contaminated sea clutter for PolSAR imagery. The CFAR detector uses a polarimetric whitening filter (PWF) to turn the multidimensional problem to a 1-D case. It uses truncation to exclude possible statistically interfering outliers and uses TS to model the remaining background samples. The algorithm does not require prior knowledge of the interfering targets, and it is performed iteratively and adaptively to derive better estimates of the polarimetric covariance matrix (although this is computationally expensive). The PWF-TS-CFAR detector provides accurate background clutter modeling, a stable false alarm property, and improves the detection performance in high-target-density situations. RadarSat2 data are used to verify our derivations, and the results are in line with the theory.

Polarimetric whitening filter (PWF) can be used to filter polarimetric synthetic aperture radar (PolSAR) images to improve the contrast between ships and sea clutter background. For this reason, the output of the filter can be used to detect ships. This paper deals with the setting of the threshold over PolSAR images filtered by the PWF. Two parameter-constant false alarm rate (2P-CFAR) is a common detection method used on whitened polarimetric images. It assumes that the probability density function (PDF) of the filtered image intensity is characterized by a log-normal distribution. However, this assumption does not always hold. In this paper, we propose a systemic analytical framework for CFAR algorithms based on PWF or multi-look PWF (MPWF). The framework covers the entire log-cumulants space in terms of the textural distributions in the product model, including the constant, gamma, inverse gamma, Fisher, beta, inverse beta, and generalized gamma distributions ($\text{G}\Gamma $Ds). We derive the analytical forms of the PDF for each of the textural distributions and the probability of false alarm (PFA). Finally, the threshold is derived by fixing the false alarm rate (FAR). Experimental results using both the simulated and real data demonstrate that the derived expressions and CFAR algorithms are valid and robust.

In the Synthetic Aperture Radar (SAR) framework many detection algorithms and techniques have been published in the recent literature; however the detection of vessels whose dimensions are in the order of the image spatial resolution is still challenging in rough sea state scenarios. This issue is addressed in the paper presented here by comparing rationale and performance of two detectors developed by the same authors: the Generalized Likelihood Ratio Test (GLRT) and the Intensity Dual-Polarization Ratio Anomaly Detector (iDPolRAD). Both detectors are tested on a dual-polarization VV/VH Interferometric Wide Swath Sentinel-1 image acquired over the Suruga Bay on the Pacific Coast of Japan. The theory is presented here and the two detectors are compared against the Cell Average-Constant False Alarm Algorithm (CA-CFAR) showing both better performance than CFAR in terms of false alarms rejection.

Ship detection is one important task in radar remote sensing. Moreover, Polarimetry shows a valuable contribution to discriminate between targets and clutter. The performance of most polarimetric detectors depends on two important factors: target clutter ratio (TCR) and speckles (or standard deviation to mean ratio of clutter background). The polarimetric matched filter (PMF) is just to maximize the TCR, while the polarimetric whitening filter (PWF) only takes the speckle reduction into consideration. In this paper, the optimal polarimetric detection filter (OPDF) is put forward, which considers maximizing the ratio of TCR to speckle. The approximate expression of the probability density function (PDF) of the OPDF is derived in closed form, so are the probability of false alarm (PFA) and the probability of detection (PD) in Wishart distribution assumption. The threshold of the OPDF detection can be easily obtained in closed form or via the bisection method. Experiments via simulated data validate the correctness of our results. The OPDF detector gives the best performance in most environments, especially in low PFA case and in the case where the statistics of targets is not the ideal Wishart distribution.

The detection of wind turbines in a strong clutter background is analyzed at variance of polarimetric synthetic-aperture radar (SAR) configurations. The area of interest is the intertidal zone near Jiangsu, China and two detectors are used, the polarimetric notch filter (PNF) and a change detector that optimizes the ratio between covariance matrices. The detection performance is quantitatively analyzed using the receiver operating characteristic (ROC) curve, while the scattering mechanisms that characterize wind turbines are analyzed using the Yamaguchi decomposition. Experimental analysis shows that: 1) wind turbines result in a nontrivial scattering mechanism and 2) full-polarimetric measurements achieve the best detection performance independently of the two detectors.

Plastic pollution in world oceans is estimated to have reached 270.000 tones, or 5.25 trillion pieces. This plastic is now ubiquitous, however due to ocean circulation patterns, it accumulates in the ocean gyres, creating “garbage patches”. This plastic debris is colonized by microorganisms which can create unique surfactants and bio-film ecosystems. Microbial colonization is the first step towards disintegration and degradation of plastic materials: a process that releases metabolic by-products from energy synthesis. These byproducts include the release of short-chain and more complex carbon molecules in the form of surfactants, which we hypothesize will affect the fluid dynamic properties of waves (change in viscosity and surface tension) and make them detectable by the SAR sensor. In this study we used Sentinel-1A and COSMO-SkyMed SAR images in selected sites of the North Pacific and North Atlantic oceans, close to the ocean gyres and away from the coastal interference. Together with SAR processing we conducted contextual image analysis, using ocean geophysical products of the sea surface temperature, surface wind, chlorophyll, wave heights and wave spectrum of the ocean surface. In addition, we started lab experiments under controlled conditions to test the behaviour of microbes colonizing the two most common marine pollutants, polyethylene (PE) and polyethylene terephthalate (PET) microplastics. The analysis of the SAR images had shown that a combination of surface wind speed and Langmuir cells- ocean circulation pattern is the main controlling factor in creating the distinct appearance of the surfactants, sea-slicks and microbial bio-films. The preliminary conclusion of our study is that SAR remote sensing may be able to detect plastic pollution in the open oceans and this method can be extended to other areas.

In this study, multi-polarimetric features extracted from dual-polarimetric (DP) Synthetic Aperture Radar (SAR) data collected by the Sentinel-l SAR mission are exploited to analyze damages due to earthquakes. First, conventional single-polarization features, based on the coherence between pre- and post-event imagery, are analyzed using both the co-polarized and the cross-polarized channels to point out that the information carried on the two channels is different. Then, a coherent dual-polarimetric feature, based on the inter-channel coherence, is proposed. Results show that the latter feature allows to detect the changes after an earthquake using only two SAR acquisitions, i.e.; before and after the earthquake. Finally, a change detection algorithm based on the eigenvalues of the difference of covariance matrices, is proposed. Experimental results show that the DP information allows to improve the results obtained by conventional single polarization change detection techniques.

Monitoring the stability of bridges under heavy traffic conditions is of paramount importance. Surveyors are interested in quantifying the amount of short-term vertical displacement due to traffic loads. A common way to measure the vertical oscillation of a bridge is by deploying reflectors/prisms on the bridge and using a total station. Alternatively lasers can be used to measure vibrations, but these are generally expensive. In this work, we are testing the use of relatively inexpensive technology based on a Stepped Frequency Continuous Waveform radar. We have acquired radar backscattering under bridges in Milton Keynes, UK and validated the results using a video camera synchronised with the radar acquisitions. Every time a truck was crossing the bridge an oscillation of around a millimetre was observed. Different trucks provided slightly different oscillations.

Icebergs represent a danger to navigation in cold waters. Detection and tracking of large icebergs using space-borne scatterometers, altimeters and synthetic aperture radar (SAR) systems have seen a large amount of work in the last decades. However, the identification of small icebergs is still challenging especially when these are embedded in sea ice. In this work, a recently proposed iceberg detector the intensity Dual-Pol Ratio Anomaly Detector (iDPolRAD) is tested using ALOS-2 L-band data. The detector is designed for dual-polarized incoherent SAR images, however in this work we want to investigate the loss of performance with respect to using coherent data. Therefore different versions of the iDPolRAD requiring quad-pol data are proposed here and tested. The ALOS-2 data were acquired on the East Coast of Greenland, where a large number of icebergs are visible in the images. The results show that different polarisations could bring different information and therefore the availability of dual-pol could improve the detection although a quad-pol algorithm will not be operational at the moment due to the limited size of the swath.

Over the past decades, the great technological advances made in airborne and space sensors have led to a significant improvement in remote sensing methods and techniques used for studying worldwide natural ecosystem disturbances. More commonly, optical sensors are chosen for detecting landscapes transformation, however, in addition to the problematics associated to this technology due the requirement of certain technical and environmental conditions (sunlight, no cloud-coverage), these systems may offer a significant lower performance when studying the post-disturbance evolution. In response to this challenge, this research aims to highlight the capabilities of Synthetic Aperture Radar - SAR satellite sensors for investigating the environmental evolution of forest areas affected by Mediterranean fire events. The use of a multitemporal analysis of ESA-Sentinel 1 SAR and Landsat 7 & Sentinel 2 optical satellite image series allowed us to explore the post-fire natural evolution of the areas affected by the Doñana national park forest fire occurred in June-July 2017 in two different ways: 1. Monitoring radar image intensity changes to evaluate environmental disturbances within the affected areas; 2. Comparing the response of both optical and radar satellite systems to the extraction of long-term environmental information. The results obtained from both the backscatter signal analysis and the systems comparison illustrate the SAR technology's efficiency in detecting and studying temporal changes in the natural conditions of the areas affected by fire events.

SAR polarimetry (PolSAR) can play an important role in monitoring agricultural fields both in terms of improving detection of specific plants conditions and providing physical information regarding the change. Such information can be used to help retrieving the phenological stage and eventually identifying stress conditions. In this work, a new change detection based on PolSAR data is first tested over time series of images acquired over agricultural fields. The methodology is based on the use of the normalised difference between covariance matrices acquired at two different instants. A diagonalisation of such matrix allows identifying the scattering mechanisms that suffer the largest change. The methodology is tested exploiting C-band quad-polarimetric RADARSAT-2 data over rice fields in Sevilla, South-West of Spain.

SAR polarimetry (PolSAR) can play an important role in change detection both in terms of improving the detection capabilities and providing physical information regarding the change. In agricultural context, such information can be used to help retrieving the phenological stage and eventually identifying stress conditions. In this work, a new change detection based on PolSAR data is proposed. The methodology is based on the use of the normalised difference between covariance matrices acquired at two different instants. A diagonalisation of such matrix allows identifying the scattering mechanisms that suffer the largest change. The methodology is tested exploiting L-band quad-polarimetric E-SAR (DLR) data from the AGRISAR 2006 campaign.

Extensive work has been carried out on detecting ships using space-borne Synthetic Aperture Radar (SAR) systems. However, the identification of small vessels is still challenging especially when the sea conditions are rough. In this work, a new detector is proposed based on dual-polarized incoherent SAR images. Small ships have a stronger cross polarization accompanied by a higher cross-over co-polarization ratio compared to sea. This is the rational at the base of the detector. The new detector is tested with dual-polarization HH/HV PINGPONG Cosmo-SkyMed images acquired over the North Sea. The test area is near Rotterdam where a large number of ships are expected.

Satellite-based synthetic aperture radar (SAR) has been proven to be an effective tool for maritime safety and security. In this framework, monitoring oil and gas offshore platforms is a key topic taken into account the high risk of accident, e.g. exposed to extreme weather conditions, and the potential threats to the environment, e.g. release of polluting material into the ocean. In this study, offshore platform monitoring is discussed using multi-polarization X-band SAR imagery. For operational purposes, the analysis is undertaken using a data set of dual-polarization TerraSAR-X/TanDEM-X (TS-X/TD-X) imagery collected over a test site in Gulf of Mexico at low and high incidence angles. Additionally, experimental Dual Receive Antenna (DRA) bistatic quad-polarization TD-X data will be used for a more in depth analysis of the backscattering properties and detection performance of different target polarimetric detectors. The motivation behind this work is the observation that, under low incidence angle and moderate wind conditions, co-polarized channels may fail in detecting offshore platforms even when fine-resolution imagery is considered. The multi-temporal dataset allows investigating the possible causes of this unexpected behavior and to draw some conclusions on the target's backscattering depending on polarization, resolution and incidence angle.

In this paper, a new algorithm for ship detection with Synthetic Aperture Radar (SAR) images is presented. We develop the proposed method by combing Principal Component Analysis (PCA) and the Geometrical Perturbation-Polarimetric Notch Filter (GP-PNF) method. In the first step, we replace the feature vector composed by the elements of the covariance matrix with more polarimetric features. Then, PCA is used to reduce the feature space. The new reduced feature vector is then used to detect ships by using the framework of the GP-PNF. In order to demonstrate the effectiveness of the proposed method, we exploited Sentinel-1 datasets. In this abstract, a dataset obtained in Gibraltar is considered. A comparison with other methods showed improvements in detection capability.

In this paper, a new algorithm for detecting ship and removing azimuth ambiguities is presented. The proposed method is developed by combing the third eigenvalue and the Geometrical Perturbation-Polarimetric Notch Filter (GP-PNF) methods. We firstly improve the GP-PNF feature vector with the third eigenvalue calculated by the eigenvalues-eigenvector decomposition method. Then, the new feature vector is used to remove azimuth ambiguities in the framework of the GP-PNF method. To demonstrate the effectiveness of the proposed method, we exploited one AIRSAR C-band dataset here. In comparing with the traditional GP-PNF method, we find our method has a better capability in removing azimuth ambiguities and detect real ships.

Quadratic forms play an important role in the development of several polarimetric and interferometric synthetic aperture radar (Pol-InSAR) methodologies, which are very powerful tools for earth observation. This paper investigates integrals of Pol-InSAR operators based on quadratic forms, with special interest on the Pol-InSAR coherence. A new operator, namely Trace Coherence, is introduced, which provides an approximation for the center of mass of the coherence region (CoRe). The latter is the locus of points on the polar plot containing all the possible coherence values. Such center of mass can be calculated as the integral of Pol-InSAR coherences over the scattering mechanisms (SMs). The trace coherence provides synthetic information regarding the partial target as one single entity. Therefore, it provides a representation, which is not dependent on the selection of one specific polarization channel. It may find application in change detection (e.g., coherent change detection and differential DEM), classification (e.g., building structure parameters), and modeling (e.g., for the retrieval of forest height). In calculating the integral of the Pol-InSAR coherences, an approximate trace coherence expression is derived and shown to improve the calculation speed by several orders of magnitude. The trace coherence approximation is investigated using Monte Carlo simulations and validated ESA (DLR) L-band quad-polarimetric data acquired during the AGRISAR 2006 campaign. The result of the analysis using simulated and real data is that the average error in approximating the integral of the coherence region is 0.025 in magnitude and 3° in phase (in scenarios with sufficiently high coherence).

Icebergs represent hazards to maritime traffic and offshore operations. Satellite synthetic aperture radar (SAR) is very valuable for the observation of polar regions, and extensive work was already carried out on detection and tracking of large icebergs. However, the identification of small icebergs is still challenging especially when these are embedded in sea ice. In this paper, a new detector is proposed based on incoherent dual-polarization SAR images. The algorithm considers the limited extension of small icebergs, which are supposed to have a stronger cross-polarization and higher cross- over copolarization ratio compared to the surrounding sea or sea ice background. The new detector is tested with two satellite systems. First, RADARSAT-2 quad-polarimetric images are analyzed to evaluate the effects of high-resolution data. Subsequently, a more exhaustive analysis is carried out using dual-polarization ground-detected Sentinel-1a extra wide swath images acquired over the time span of two months. The test areas are in the east coast of Greenland, where several icebergs have been observed. A quantitative analysis and a comparison with a detector using only the cross-polarization channel are carried out, exploiting grounded icebergs as test targets. The proposed methodology improves the contrast between icebergs and sea ice clutter by up to 75 times. This returns an improved probability of detection.

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.

Quadratic forms play an important role in Polarimetric and Interferometric Synthetic Aperture Radar (Pol-InSAR) images. This work is aimed at solving (rigorously and with approximations) the integrals of quadratic forms. Specifically, it is possible to derive that the integral of the quadratic form of covariance matrices (i.e. power of a polarization channel) is equal to the third part of the matrix Trace. Additionally, the integral of the Pol-InSAR coherence (expressed with quadratic forms) can be approximated with the same expression where the quadratic forms are substituted by Trace operators. The derived equations are tested on real ESAR (DLR) quad-polarimetric data.

Icebergs are hazards to lives and goods during navigation in cold waters. In the context of iceberg detection with SAR images, an extensive work was carried out for the detection of large icebergs, but the identification of small bergs or target embedded in sea ice is till difficult. In this work, a new detector is propose to tackle this issue based on dual-polarimetric incoherent (i.e. detected) images. The algorithm is based on the principle that small icebergs are contained in a limited area and they are supposed to have a volume contribution that is higher compared to the sea or sea-ice background. The detector is tested on RADARSAT-2 quad-polarimetric data, where only the multi-looked intensities of the HH and HV channels are used.

Wetlands are very valuable ecosystem which may present fast dynamics. This study is focused on the observation of the Kafue Flats exploiting TanDEM-X polarimetric and interfer-ometric data. Several analysis are carried out considering classification, change detection and Digital Elevation Model (DEM) estimation. Specifically, it is possible to observe that differences between single-pass DEM acquired during different dates can provide promising estimations of the flood water level and extent (provided that the water is partially covered by vegetation). Additionally, the polarimetric information can add details regarding small areas in the data where swamps are presents also in dry seasons.

Synthetic aperture radar plays a vital role in ship detection due to the possibility of acquiring high-resolution images at nighttime and under cloud cover. This letter is focused on improving ship detection, exploiting the capability of TanDEM-X to collect interferometric data. Currently, along-track interferometry is used to estimate the speed of ocean surface currents or vessels. The detection of ships plays an important role in the retrieval of vessel speed and is mostly executed exploiting only one of the TanDEM-X images (i.e., not taking advantage of the availability of a second interferometric image). The aim of this study is to extend the capabilities of a ship detector previously developed by the authors, namely, geometrical perturbation-polarimetric notch filter (GP-PNF), to include single-pass interferometric information acquired by TanDEM-X. Interestingly, such enhancement makes it possible to employ the GP-PNF with single-polarization data as well. The proposed algorithms and their statistical behavior are tested on five Tandem-X dual-polarimetric HH/VV scenes acquired in the North Sea. The detection results are validated, exploiting the Automatic Identification System location of vessels. All of the new GP-PNF versions show good performance and provide larger vessel-sea contrast compared with single-channel detectors.

Ship detection is an important topic in remote sensing, and synthetic aperture radar (SAR) has a valuable contribution, allowing detection at nighttime and with almost any weather conditions. In addition, polarimetry can play a significant role considering its capability to discriminate between different targets. Recently, a new ship detector exploiting polarimetric information has been developed, namely, the Geometrical Perturbation-Polarimetric Notch Filter (GP-PNF). This work is focused on devising two statistical tests for the GP-PNF. The latter allow an automatic and adaptive selection of the detector threshold. Initially, the probability density function (pdf) of the detector is analytically derived. Finally, the Neyman-Pearson lemma is exploited to set the threshold calculating probabilities using the clutter pdf (i.e., a constant false-alarm rate) and a likelihood ratio. The goodness of fit of the clutter pdf is tested with four real SAR data sets acquired by the RADARSAT-2 and the TanDEM-X satellites. The former images are quad-polarimetric, whereas the latter are dual-polarimetric HH/VV. The data are accompanied by the Automatic Identification System (AIS) location of vessels, which facilitates the validation of the detection masks. It can be observed that the pdfs fit the data histograms, and they pass the two sample Kolmogorov-Smirnov and χ2tests.

Ship detection is an important topic for security and surveillance of maritime and costal areas. A solution exploiting satellite-borne SAR sensors is particularly interesting, because it offers wide scale surveillance capabilities, which are not reliant on solar illumination and are rather independent of weather conditions ([1], [2], [3]). In SAR images, the main feature of a ship is a relatively large backscattering signal, which is usually brighter in comparison with the sea background. This led to the idea of using the intensity contrast as a feature to discriminate between targets and sea clutter. Several methodologies were proposed ([1], [3]). Most of these techniques set a statistical test between target and clutter background. Recently, the several ship detectors were proposed that exploits the property of SAR images to perform detection. In this work, two methodologies used for coherent scatterer detection are tested for the first time for ship detection and a comparison of ship detectors based on spectral analysis is performed over L-band ALOS date accompanied by a ground survey.

The possibility to detect changes in land cover with remote sensing is particularly valuable considering the current availability of long time series of data. Synthetic Aperture Radar (SAR) can play an important role in this context since it can acquire complete time series without limitations of cloud cover. Additionally, polarimetry has the potential to improve significantly the detection capability, allowing the discrimination between different polarimetric targets. This paper is focused on developing two new methodologies for testing the stability of observed targets (i.e., equiscattering-mechanism hypothesis) and change detection. Both the algorithms adopt a Lagrange optimization, which can be performed with two eigenproblems. Interestingly, the two optimizations share the same eigenvectors. Three statistical tests are proposed to set the threshold for the change detector. Two of them are mostly aimed at point targets, and one is more suited for distributed targets.

Remote sensing has a large relevance in sea ice observation. In this context, Synthetic Aperture Radar may play a valuable rule due to the possibility to achieve high resolution images not reliant on solar illumination and relatively independent of weather conditions. This paper deals with the exploitation of polarimetry to solve some ambiguities that may rise in SAR observations due to the orientation angle of the target. The quad-polarimetric datasets exploited in this work were acquired in 2007 by the DLR E-SAR systems in L-band over Svalbard. It is showed that taking into account orientation effects has potentials to improve the final ice classification.

Ship detection with Synthetic Aperture Radar is a largely investigated topic and a series of operational algorithms are currently exploited by the users community. Polarimetry may play an important rule in this context, since the next generation of SAR satellites will be able to perform large swath polarimetric (either dual or compact) acquisitions. In this work, a comparison of promising ship detectors employing polarimetric information is performed over an ALOS-PALSAR quad-polarimetric dataset acquired over Tokyo Bay (October 2008). Interestingly, a survey of the area is available allowing to provide some quantitative comparison of the different detectors. The results show that the quad polarimetric detectors return better detection performance than the dualor single-polarimetric ones.

In this study recent advances in Synthetic Aperture Radar (SAR) polarimetry to observe sea oil slicks and man-made metallic targets are reviewed in the frame of the ESA funded PolSAR-Ap project. The most up-to-dated quad-pol approaches to observe sea oil slicks and metallic targets are reviewed and their performance is discussed against conventional single- and dual-pol approaches using actual L- and C-band Single Look Complex (SLC) SAR data. The unique benefits of quad-pol SAR data to observe both oil slicks and metallic targets are clearly shown.

In modern society, the anthropogenic influences on ecosystems are central points to understand the evolution of our planet. A polarimetric synthetic aperture radar may have a significant contribution in tackling problems concerning land use change, since such data are available with any-weather conditions. Additionally, the discrimination capability can be enhanced by the polarimetric analysis. Recently, an algorithm able to identify targets scattering an electromagnetic wave with any degree of polarization has been developed, which makes use of a vector rearrangement of the elements of the coherency matrix. In the present work, this target detector is modified to perform change detection between two polarimetric acquisitions, for land use monitoring purposes. Regarding the selection of the detector parameters, a physical rationale is followed, developing a new parameterization of the algebraic space where the detector is defined. As it will be illustrated in the following, this space is 6-D complex with restrictions due to the physical feasibility of the vectors. Specifically, a link between the detector parameters and the angle differences of the eigenvector model is obtained. Moreover, a dual polarimetric version of the change detector is developed, in case quad-polarimetric data are not available. With the purpose of testing the methodology, a variety of data sets were exploited: quad-polarimetric airborne data at L-band (E-SAR), quad-polarimetric satellite data at C-band (Radarsat-2), and dual-polarimetric satellite data at X-band (TerraSAR-X). The algorithm results show agreement with the available information about land changes. Moreover, a comparison with a known change detector based on the maximum likelihood ratio is presented, providing improvements in some conditions. The two methodologies differ in the analysis of the total amplitude of the backscattering, where the proposed algorithm does not take this into consideration.

This paper is focused on the polarimetric change detector (PCD) based on perturbation filters already developed by the authors. The highlight of the PCD is its independence of the overall amplitude of the coherence matrix (scaling factor), focusing exclusively on the polarimetric information. Here, the relationship between the PCD and the polarimetric and interferometric (Pol-InSAR) coherence is investigated. Many Pol-InSAR algorithms are based on the coherence operator, therefore such parameterisation would ease the process of setting the detector threshold. The methodology is tested using E-SAR AgriSAR, L-band quad polarimetric data, showing agreement with the observed changes and the actual interferometric coherence.

Detection of icebergs is a major topic for surveillance of polar maritime areas and coastlines. Synthetic Aperture Radar (SAR) has been shown to be particularly useful because of its all-weather and night capability. In this paper a methodology based on the polarimetric perturbation analysis is presented. The algorithm can be considered to be a negative filter focused on sea. Consequently, all the features which have a polarimetric behavior different from the sea are detected and considered as targets (i.e. ships, buoys, icebergs). In this work the notch filter is focused on icebergs detection. A test with TerraSAR-X quad polarimetric data was performed over a scene acquired on the Canadian coastal area (Northwest Passages). The detections showed promising results, with identification of areas where large and small icebergs appear, but also ridges seems to be detected.

The use of polarimetric and interferometric SAR (Pol-InSAR) has been demonstrated to bring significant advantages for several remote sensing applications. Sometimes, the hypothesis that the scattering mechanisms (SM) do not suffer large changes in the two acquisitions is performed (ESM). On the other hand, a different set of algorithms should be employed. In this paper a new methodology is proposed to identify errors consequence of possible changes. The algorithm performs a Lagrange optimization of an error factor of the Pol-InSAR coherences, returning also the SM that change maximally and minimally during the two acquisitions. The algorithm was tested on E-SAR L-band data acquired during the AgriSAR 2006 campaign.

Target detectors using polarimetry are often focused on single targets, since these can be characterized in a simpler and deterministic way. The algorithm proposed in this paper is aimed at the more difficult problem of partial-target detection (i.e., targets with arbitrary degree of polarization). The authors have already proposed a single-target detector employing filters based on a geometrical perturbation. In order to enhance the algorithm to the detection of partial targets, a new vector formalism is introduced. The latter is similar to the one exploited for single targets but suitable for complete characterization of partial targets. A new feature vector is generated starting from the covariance matrix and exploited for the perturbation method. Validation against L-band fully polarimetric airborne E-SAR and ALOS PALSAR data and X-band dual-polarimetric TerraSAR-X data is provided with significant agreement with the expected results. Additionally, a comparison with the supervised Wishart classifier is presented revealing improvements.

Several POLInSAR methodologies are based on the concept of polarimetric and interferometric coherence [1]. Many of them assume the scattering mechanisms observed at the two interferometric acquisitions are the same (i.e. equi-scattering mechanism, ESM). However, if the scatterers change, different approaches must be employed. In order to select the appropriate set of algorithms, a test of target stability should be performed as pre-processing. The authors already devel oped a polarimetric change detector [2], based on perturbation analysis. In the present paper, the change detector and the parameterization introduced in [2] are modified in order to perform the ESM test on dual polarimetric TanDEM-X data. Unfortunately, TanDEM-X data were not available at the mo ment of compiling this paper, so the algorithm was tested on TerraSAR-X HHIVV images.

Ship detection is a key topic for the surveillance of coastal areas and synthetic aperture radar (SAR) presents an advantageous technology for this application, because the observations cover relatively large areas and are independent of atmospheric cloud conditions and solar illumination. Recently a new generation of satellites has become available with enhanced SAR capabilities and our algorithm detects vessels by exploiting the difference between the polarimetric SAR signature of sea clutter and ships. In this paper, the algorithm that was originally used with SAR data at the C-Band frequency has been enhanced for use with high resolution X-band data such as TerraSAR-X. Moreover a dual polarimetric version of the algorithm is proposed, which has associated costs and benefits that are interesting to compare with the quad polarimetric counterpart.

In this paper, biomass estimations are compared using quadpol ALOS PALSAR, TerraSAR-X and LIDAR data over Glen Affric, Scotland. Biomass was retrieved from the ALOS PALSAR and TerraSAR-X data using Yamaguchi decomposition to obtain useful information about the scattering mechanisms. A regression equation is obtained from the establishment of a relationship between the ratio of volume and surface scattering mechanism and the biomass obtained from fieldwork. Since the study site is a mountainous region, the terrain slope effects need to be compensated before retrieving the biomass. For LIDAR, the vertical structure of both the underlying topography and the forest structure were generated to estimate the biomass allometrically. Validation on the results of the biomass estimation was done by comparing the biomass estimated using ALOS PALSAR, TerraSAR-X and LIDAR. The results suggest that in some areas the biomass retrievals are broadly comparable.

Microwave remote sensing (MRS) has been widely employed for biomass estimation. However, the retrieval of biomass related parameters for fractional areas with both trees and bare ground areas remains a problem unsolved. The Freeman-Durden decomposition (FD) was developed for applications with vegetation and could make efficient use of both amplitude and phase information provided by polarized MRS data. Based on FD, the authors devised a methodology to solve the problematic caused by discontinuous distribution of vegetation. Effectiveness of the proposed method was tested by experiments on RADARSAT-2 quad-pol images and corresponding in situ data.

Target detectors using polarimetry are often focused on single (coherent) targets, since these are the ones that can be more simply characterized polarimetrically. The new proposed algorithm is aimed at the more difficult problem of partial target detection (i.e. targets with any degree of polarization). A new feature vector is defined starting from the coherency matrix, and then a perturbation method is performed. Starting from the partial target detection, a novel classification algorithm is proposed. The validation is carried out against fully polarimetric satellite data. In particular, X band TerraSAR-X and L band ALOS PALSAR are employed, providing significant agreement with the expected results and the supervised Wishart classifier.

Target detection of marine feature is a major topic for the security and monitoring of coastlines. Synthetic Aperture Radar (SAR) has been shown to be particularly useful for this application because of its all-weather and night capability. In this paper a new ship and iceberg detection methodology is described. The algorithm proposed is based on a perturbation analysis in the target space recently developed and published by the authors, which was focused on land based target detection. The algorithm can be considered to be a negative filter focused on sea. Consequently, all the features which have a polarimetric behaviour different from the sea are detected. To demonstrate and validate the technique two RadarSat Fine Quad-Pol mode scenes were acquired off the south coast of the UK at Portsmouth harbour. An extensive ground truth campaign was also conducted that was coincident with these acquisitions. Portsmouth is one of the busiest harbours in the UK and this afforded the opportunity to capture a wide range of vessel sizes and types for analysis.

The contribution of synthetic aperture radar polarimetry in target detection is described and found to add valuable information. A new target detection methodology that makes novel use of the polarization fork of the target is described. The detector is based on a correlation procedure in the target space, and other target representations (e.g., Huynen parameters or ¿ angle) can be employed. The mathematical formulation is general and can be applied to any kind of single target; however, in this paper, the detection is optimized for the odd and even bounces (the first two elements of the Pauli scattering vector) and for the oriented dipoles. Validation against real data shows significant agreement with the expected results based on the theoretical description.

A new target detection methodology is described that makes novel use of the polarization fork of the target. The mathematical formulation is general and can be applied to any kind of single target as long as its expression in the target space is known. Aim of this paper is to present a standard procedure to set the detector parameters for any target of interest. The algorithm makes use of the Gram-Schmidt ortho-normalization in order to set the appropriate basis for the polarimetric space. Validation against real data shows significant agreement with the expected results based on the theoretical description.

Tropical savannas cover 20% of the Earth's land surface and are important ecosystems in the global carbon cycle due to their high productivity. This paper evaluates the use of SAR for estimating above-ground biomass of the woody vegetation in heterogeneous tropical savanna woodland in Belize, Central America. Single-pass shortwave InSAR data used are X-band (Intermap) and C-band (AIRSAR and SRTM). L- and P-band SAR backscatter data are from AIRSAR. Results show that SAR backscatter has a relatively low correlation to above ground biomass in the sparse savanna woodlands. Retrieved canopy heights from both X- and C-band InSAR give a better representation of the spatial distribution of AGB, but cannot be used to estimate biomass directly due to the heterogeneity of the canopy.