Validating a notch filter for detection of targets at sea with ALOS-PALSAR data: Tokyo Bay

The surveillance of maritime areas is a major topic for security aimed at fighting issues as illegal trafficking, illegal fishing, piracy, etc. In this context, Synthetic Aperture Radar (SAR) has proven to be particularly beneficial due to its all-weather and night time acquisition capabilities. Moreover, the recent generation of satellites can provide high quality images with high resolution and polarimetric capabilities. This paper is devoted to the validation of a recently developed ship detector, the Geometrical Perturbations Polarimetric Notch Filter (GP-PNF) exploiting L-band polarimetric data. The algorithm is able to isolate the return coming from the sea background and trigger a detection if a target with different polarimetric behavior is present. Moreover, the algorithm is adaptive and is able to account for changes of sea clutter both in polarimetry and intensity. In this work, the GP-PNF is tested and validated for the first time ever with L-band data, exploiting one ALOS-PALSAR quad-pol dataset acquired on the 9th of October 2008 in Tokyo Bay. One of the motivations of the analysis is also the attempt of testing the suitability of GP-PNF to be used with the new generations of L-band satellites (e.g. ALOS-2). The acquisitions are accompanied by a ground truth performed with a video survey. A comparison with two other detectors is presented, one exploiting a single polarimetric channel and the other considering quad-polarimetric data. Moreover, a test exploiting dual-polarimetric modes (HH/VV and HH/HV) is performed. The GP-PNF shows the capability to detect targets presenting pixel intensity smaller than the surrounding sea clutter in some polarimetric channels. Finally, the quad-polarimetric GP-PNF outperformed in some situations the other two detectors

A Depolarization Ratio Anomaly Detector to identify icebergs in sea ice using dual-polarization SAR images

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 work, 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 co-polarization ratio compared to the surrounding sea or sea ice background. The new detector is tested with two satellite systems. Firstly, 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 on 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 is 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

Quad-Polarimetric Multi-Scale Analysis of Icebergs in ALOS-2 SAR Data: A Comparison between Icebergs in West and East Greenland

Icebergs are ocean hazards which require extensive monitoring. Synthetic Aperture Radar (SAR) satellites can help with this, however, SAR backscattering is strongly influenced by the properties of icebergs, together with meteorological and environmental conditions. In this work, we used five images of quad-pol ALOS-2/PALSAR-2 SAR data to analyse 1332 icebergs in five locations in west and east Greenland. We investigate the backscatter and polarimetric behaviour, by using several observables and decompositions such as the Cloude–Pottier eigenvalue/eigenvector and Yamaguchi model-based decompositions. Our results show that those icebergs can contain a variety of scattering mechanisms at L-band. However, the most common scattering mechanism for icebergs is surface scattering, with the second most dominant volume scattering (or more generally, clouds of dipoles). In some cases, we observed a double bounce dominance, but this is not as common. Interestingly, we identified that different locations (e.g., glaciers) produce icebergs with different polarimetric characteristics. We also performed a multi-scale analysis using boxcar 5 × 5 and 11 × 11 window sizes and this revealed that depending on locations (and therefore, characteristics) icebergs can be a collection of strong scatterers that are packed in a denser or less dense way. This gives hope for using quad-pol polarimetry to provide some iceberg classifications in the future

Polarimetric SAR for the monitoring of agricultural crops

The monitoring of agricultural crops is a matter of great importance. Remote sensing has been unanimously recognized as one of the most important techniques for agricultural crops monitoring. Within the framework of active remote sensing, the capabilities of the Synthetic Aperture Radar (SAR) to provide fine spatial resolution and a wide area coverage, both in day and night time and almost under all weather conditions, make it a key tool for agricultural applications, including the monitoring and the estimation of phenological stages of crops. The monitoring of crop phenology is fundamental for the planning and the triggering of cultivation practices, since they require timely information about the crop conditions along the cultivation cycle. Due to the sensitivity of polarization of microwaves to crop structure and dielectric properties of the canopy, which in turn depend on the crop type, retrieval of phenology of agricultural crops by means of polarimetric SAR measurements is a promising application of this technology, especially after the launch of a number of polarimetric satellite sensors. In this thesis C-band polarimetric SAR measurements are used to estimate pheno- logical stages of agricultural crops. The behavior of polarimetric SAR observables at different growth stages is analyzed and then estimation procedures, aimed at the retrieval of such stages, are defined. The second topic on which this thesis is focused on is the land cover types discrimi- nation by means of X-band multi-polarization SAR data

Polarimetric SAR for the monitoring of agricultural crops

The monitoring of agricultural crops is a matter of great importance. Remote sensing has been unanimously recognized as one of the most important techniques for agricultural crops monitoring. Within the framework of active remote sensing, the capabilities of the Synthetic Aperture Radar (SAR) to provide fine spatial resolution and a wide area coverage, both in day and night time and almost under all weather conditions, make it a key tool for agricultural applications, including the monitoring and the estimation of phenological stages of crops. The monitoring of crop phenology is fundamental for the planning and the triggering of cultivation practices, since they require timely information about the crop conditions along the cultivation cycle. Due to the sensitivity of polarization of microwaves to crop structure and dielectric properties of the canopy, which in turn depend on the crop type, retrieval of phenology of agricultural crops by means of polarimetric SAR measurements is a promising application of this technology, especially after the launch of a number of polarimetric satellite sensors. In this thesis C-band polarimetric SAR measurements are used to estimate pheno- logical stages of agricultural crops. The behavior of polarimetric SAR observables at different growth stages is analyzed and then estimation procedures, aimed at the retrieval of such stages, are defined. The second topic on which this thesis is focused on is the land cover types discrimi- nation by means of X-band multi-polarization SAR data

Quad polarimetric synthetic aperture radar analysis of icebergs in Greenland and Svalbard

Polarimetric synthetic aperture radar (PolSAR) has been widely used in ocean and cryospheric applications. This is because, PolSAR can be used in all-day operations and in areas of cloud cover, and therefore can provide valuable large-scale monitoring in polar regions, which is very helpful to shipping and offshore maritime operations. In the last decades, attention has turned to the potential of PolSAR to detect icebergs in the Arctic since they are a major hazard to vessels. However, there is a substantial lack of literature exploring the potentialities of PolSAR and the understanding of iceberg scattering mechanisms. Additionally, it is not known if high resolution PolSAR can be used to detect icebergs smaller than 120 metres. This thesis aims to improve the knowledge of the use of PolSAR scattering mechanisms of icebergs, and detection of small icebergs. First, an introduction to PolSAR is outlined in chapter two, and monitoring of icebergs is presented in chapter three. The first data chapter (Chapter 4) is focused on developing a multi-scale analysis of icebergs using parameters from the Cloude-Pottier and the Yamaguchi decompositions, the polarimetric span and the Pauli scattering vector. This method is carried out using ALOS-2 PALSAR quad polarimetric L-band SAR on icebergs in Greenland. This approach outlines the good potential for using PolSAR for future iceberg classification. One of the main important outcomes is that icebergs are composed by a combination of single targets, which therefore may require a more complex way of processing SAR data to properly extract physical information. In chapter five, the problem of detecting icebergs is addressed by introducing six state-of-the-art detectors previously applied to vessel monitoring. These detectors are the Dual Intensity Polarisation Ratio Anomaly Detector (iDPolRAD), Polarimetric Notch Filter (PNF), Polarimetric Matched Filter (PMF), reflection symmetry (sym), Optimal Polarimetric Detector (OPD) and the Polarimetric Whitening Filter (PWF). Cloude-Pottier entropy, and first and third eigenvalues (eig1 and eig3) of the coherency matrix are also utilised as parameters for comparison. This approach uses the same ALOS-2 dataset, but also evaluates detection performance in two scenarios: icebergs in open ocean, and in sea ice. Polarimetric modes (quad-pol, dual-pol, and single intensities) are also considered for comparison. Currently it is very difficult to detect icebergs less than 120 metres in length using this approach, due to the scattering mechanisms of icebergs and sea ice being very similar. However, it was possible to obtain detection performances of the OPD and PWF, which both showed a Probability of Detection (PF) of 0.99 when the Probability of False Alarms (PF) was set to 10-5 in open ocean. Similarly, in dual pol images, the PWF gave the best performance with a PD of 0.90. Results in sea ice found eig3 to be the best detector with a PD of 0.90 while in dual-pol mode, iDPolRAD gave a PD of 0.978. Single intensity detector performance found the HV channel gave the best detection with a PD of 0.99 in open ocean and 0.87 in sea ice. In the previous two approaches, only satellite data is used. However, in chapter six, data from a ground-based Ku-band Gamma Portable Radio Interferometer (GPRI) instrument is introduced, providing images that are synchronised with the satellite acquisitions. In this approach, the same six detectors are applied to three multitemporal RADARSAT-2 quad pol C-band SAR images on icebergs in Kongsfjorden, Svalbard to evaluate the detection performance within a changing fjord environment. As before, we also make use of Cloude-Pottier entropy, eig1 and eig3. Finally, we evaluate the target-to-clutter ratio (TCR) of the icebergs and check for correlation between the backscattering coefficients and the iceberg dimension. The results obtained from this thesis present original additions to the literature that contributes to the understanding of PolSAR in cryospheric applications. Although these methods are applied to PolSAR and ground-based radar on vessels, they have been applied for the first time on icebergs in this thesis. To summarise, the main findings are that icebergs cannot be represented as single or partial targets, but they do exhibit a collection of single targets clustered together. This result leads to the fact that entropy is not sufficient as a parameter to detect icebergs. Detection results show that the OPD and PWF detectors perform best in an open ocean setting and using quad-pol mode. These results are degraded in dual-pol mode, while single intensity detection is best in the HV cross polarisation channel. When these detectors are applied to the RADARSAT-2 in Svalbard, the OPD and PWF detectors also perform best with PD values ranging between 0.5-0.75 for a PF of 0.01-0.05. However, the sea ice present in the fjord degrades performance across all detectors. Correlation plots with iceberg size show that a regression is not straightforward and Computer Vision methodologies may work best for this

Oil spill and ship detection using high resolution polarimetric X-band SAR data

Among illegal human activities, marine pollution and target detection are the key concern of Maritime Security and Safety. This thesis deals with oil spill and ship detection using high resolution X-band polarimetric SAR (PolSAR). Polarimetry aims at analysing the polarization state of a wave field, in order to obtain physical information from the observed object. In this dissertation PolSAR techniques are suggested as improvement of the current State-of-the-Art of SAR marine pollution and target detection, by examining in depth Near Real Time suitability

Learning to Estimate Sea Ice Concentration from SAR Imagery

Through the growing interest in the Arctic for shipping, mining and climate research, large-scale high quality ice concentration is of great interest. Due to the unavailability of suitable ice concentration estimation algorithms, ice concentration maps are interpreted from synthetic aperture radar (SAR) images manually by ice experts for operational uses. An automatic ice concentration estimation algorithm is required for accurate large-scale ice mapping. In this thesis, a set of algorithms are developed aiming at operational ice concentration estimation from SAR images. The major difficulty in designing a robust algorithm for ice concentration estimation from SAR images is the constantly changing SAR image features of ice and water in time and location. This difficulty is addressed by learning features instead of designing features from SAR images. A set of convolutional neural network based ice concentration estima- tion algorithms are developed to learn multi-scale SAR image features and simultaneously regress ice concentration from the learned image features. We first demonstrated the capa- bility of CNNs in ice concentration estimation from SAR images when trained using image analysis charts as ground truth. Then the model is further improved by taking into account the errors in the image analysis charts. Ice concentration estimates with improved robust- ness to training samples errors, accuracy and scale of details are obtained. The robustness of the developed methods are further demonstrated in the melt season of the Beaufort Sea, where reasonable ice concentration estimates are acquired. In order to reduce the model training time, it is desired to reuse existing models. The model transferability is evaluated and suggestions on using existing models to accelerate the training process are given, which is shown to reduce the training time by over 10 times in our case

Microwave Satellite Measurements for Coastal Area and Extreme Weather Monitoring

In this project report, the main outcomes relevant to the Sino-European Dragon-4 cooperation project ID 32235 “Microwave satellite measurements for coastal area and extreme weather monitoring” are reported. The project aimed at strengthening the Sino-European research cooperation in the exploitation of European Space Agency, Chinese and third-party mission Earth Observation (EO) microwave satellite data. The latter were exploited to perform an effective monitoring of coastal areas, even under extreme weather conditions. An integrated multifrequency/polarization approach based on complementary microwave sensors (e.g., Synthetic Aperture Radar, scatterometer, radiometer), together with ancillary information coming from independent sources, i.e., optical imagery, numerical simulations and ground measurements, was designed. In this framework, several tasks were addressed including marine target detection, sea pollution, sea surface wind estimation and coastline extraction/classification. The main outcomes are both theoretical (i.e., new models and algorithms were developed) and applicative (i.e., user-friendly maps were provided to the end-user community of coastal area management according to smart processing of remotely sensed data). The scientific relevance consists in the development of new algorithms, the effectiveness and robustness of which were verified on actual microwave measurements, and the improvement of existing methodologies to deal with challenging test cases