A method to improve high-resolution sea ice drift retrievals in the presence of deformation zones

Source at: http://doi.org/10.3390/rs9070718 Retrievals of sea ice drift from synthetic aperture radar (SAR) images at high spatial resolution are valuable for understanding kinematic behavior and deformation processes of the ice at different spatial scales. Ice deformation causes temporal changes in patterns observed in sequences of SAR images; which makes it difficult to retrieve ice displacement with algorithms based on correlation and feature identification techniques. Here, we propose two extensions to a pattern matching algorithm, with the objective to improve the reliability of the retrieved sea ice drift field at spatial resolutions of a few hundred meters. Firstly, we extended a reliability assessment proposed in an earlier study, which is based on analyzing texture and correlation parameters of SAR image pairs, with the aim to reject unreliable pattern matches. The second step is specifically adapted to the presence of deformation features to avoid the erasing of discontinuities in the drift field. We suggest an adapted detection scheme that identifies linear deformation features (LDFs) in the drift vector field, and detects and replaces outliers after considering the presence of such LDFs in their neighborhood. We validate the improvement of our pattern matching algorithm by comparing the automatically retrieved drift to manually derived reference data for three SAR scenes acquired over different sea ice covered regions

Object-based detection of linear kinematic features in sea ice

Source at: https://doi.org/10.3390/rs9050493 Inhomogenities in the sea ice motion field cause deformation zones, such as leads, cracks and pressure ridges. Due to their long and often narrow shape, those structures are referred to as Linear Kinematic Features (LKFs). In this paper we specifically address the identification and characterization of variations and discontinuities in the spatial distribution of the total deformation, which appear as LKFs. The distribution of LKFs in the ice cover of the polar oceans is an important factor influencing the exchange of heat and matter at the ocean-atmosphere interface. Current analyses of the sea ice deformation field often ignore the spatial/geographical context of individual structures, e.g., their orientation relative to adjacent deformation zones. In this study, we adapt image processing techniques to develop a method for LKF detection which is able to resolve individual features. The data are vectorized to obtain results on an object-based level. We then apply a semantic postprocessing step to determine the angle of junctions and between crossing structures. The proposed object detection method is carefully validated. We found a localization uncertainty of 0.75 pixel and a length error of 12% in the identified LKFs. The detected features can be individually traced to their geographical position. Thus, a wide variety of new metrics for ice deformation can be easily derived, including spatial parameters as well as the temporal stability of individual features

Method for detection of leads from Sentinel-1 SAR images

Source at https://doi.org/10.1017/aog.2018.6.The presence of leads with open water or thin ice is an important feature of the Arctic sea ice cover. Leads regulate the heat, gas and moisture fluxes between the ocean and atmosphere and are areas of high ice growth rates during periods of freezing conditions. Here, an algorithm providing an automatic lead detection based on synthetic aperture radar images is described that can be applied to a wide range of Sentinel-1 scenes. By using both the HH and the HV channels instead of single co-polarised observations the algorithm is able to classify more leads correctly. The lead classification algorithm is based on polarimetric features and textural features derived from the grey-level co-occurrence matrix. The Random Forest classifier is used to investigate the importance of the individual features for lead detection. The precision–recall curve representing the quality of the classification is used to define threshold for a binary lead/sea ice classification. The algorithm is able to produce a lead classification with more that 90% precision with 60% of all leads classified. The precision can be increased by the cost of the amount of leads detected. Results are evaluated based on comparisons with Sentinel-2 optical satellite data

Automatic Selection of Relevant Attributes for Multi-Sensor Remote Sensing Analysis: A Case Study on Sea Ice Classification

It is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal set of image attributes which provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. Comprehensive studies have been focused on the analysis of relevant features for sea ice analysis obtained from different sensors, especially synthetic aperture radar. However, the outcomes of these studies are mostly data and application-dependent and can, therefore, rarely be generalized. In this article, we employ a feature selection method based on graph Laplacians, which is fully automatic and easy to implement. The proposed approach assesses relevant information on a global and local level using two metrics and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. In the recent study, we investigate the effectiveness of this approach for sea ice classification, using different multi-sensor data combinations. Experiments show the advantage of applying multi-sensor data sets and demonstrate that the attributes selected by our method result in high classification accuracies. We demonstrate that our approach automatically considers varying technical, sensor-specific, environmental, and sea ice conditions by employing flexible and adaptive feature selection method as a pre-processing step

Sea ice segmentation using Tandem-X pursuit mono static and alternative bistatic modes

Accepted manuscript version. Source at https://doi.org/10.1109/IGARSS.2017.8126964In this paper we investigate interferometric pairs of SAR images acquired by Tandem-X with the monostatic pursuit and the alternative bistatic modes for sea ice segmentation. The individual SAR images are modelled as non-Gaussian, and from the modelled data different features are extracted, stacked together and clustered. The interferometric coherence is regarded as an additional feature and utilized for clustering. In addition to complementing the information extracted from the individual images, the interferometric coherence is found to be capable of discriminating between open water and sea ice, as well as between different ice types

Dynamics of the Terra Nova Bay Polynya: The potential of multi-sensor satellite observations

Research on processes leading to formation, maintenance, and disappearance of polynyas in the Polar Regions benefits significantly from the use of different types of remote sensing data. The Sentinels of the European Space Agency (ESA), together with other satellite missions, provide a variety of data from different parts of the electromagnetic spectrum, at different spatial scales, and with different temporal resolutions. In a case study we demonstrate the advantage of merging data from different spaceborne instruments for analysing ice conditions and ice dynamics in and around the frequently occurring Terra Nova Bay Polynya (TNBP) in the Ross Sea in the Antarctic. Starting with a list of polynya parameters that are typically retrieved from satellite images, we assess the usefulness of different sensor types. On regional scales (several 100 km), passive microwave radiometers provide a viewon the mutual influence of the three Ross Sea polynyas on sea ice drift and deformation patterns. Optical sensors with meter-scale resolution, on the other hand, allow very localized analyses of different polynya zones. The combination of different ranges of the electromagnetic spectrum is essential for recognition and classification of ice types and structures. Radar images togetherwith data fromthermal infrared sensors, operated at tens to hundreds of meters resolution, improve the separation of the outlet zone of the polynya fromthe adjacent pack ice. The direct comparison of radar and passive microwave images reveals the visibility of deformed ice zone in the latter. A sequence of radar images was employed to retrieve ice drift around the TNB, which allows analysing the temporal changes of the polynya area and the extension and structure of the outlet zone as well as ice movements and deformation that are influenced by the katabatic winds

Impact of sea ice drift retrieval errors, discretization and grid type on calculations of ice deformation

We studied two issues to be considered in the calculation of parameters characterizing sea ice deformation: the effect of uncertainties in an automatically retrieved sea ice drift field, and the influence of the type of drift vector grid. Sea ice deformation changes the local ice mass balance and the interaction between atmosphere, ice, and ocean, and constitutes a hazard to marine traffic and operations. Due to numerical effects, the results of deformation retrievals may predict, e.g., openings and closings of the ice cover that do not exist in reality. We focus specifically on fields of ice drift obtained from synthetic aperture radar (SAR) imagery and analyze the Propagated Drift Retrieval Error (PDRE) and the Boundary Definition Error (BDE). From the theory of error propagation, the PDRE for the calculated deformation parameters can be estimated. To quantify the BDE, we devise five different grid types and compare theoretical expectation and numerical results for different deformation parameters assuming three scenarios: pure divergence, pure shear, and a mixture of both. Our findings for both sources of error help to set up optimal deformation retrieval schemes and are also useful for other applications working with vector fields and scalar parameters derived therefrom

An Optimal Decision-Tree Design Strategy and Its Application to Sea Ice Classification from SAR Imagery

We introduce the fully automatic design of a numerically optimized decision-tree algorithm and demonstrate its application to sea ice classification from SAR data. In the decision tree, an initial multi-class classification problem is split up into a sequence of binary problems. Each branch of the tree separates one single class from all other remaining classes, using a class-specific selected feature set. We optimize the order of classification steps and the feature sets by combining classification accuracy and sequential search algorithms, looping over all remaining features in each branch. The proposed strategy can be adapted to different types of classifiers and measures for the class separability. In this study, we use a Bayesian classifier with non-parametric kernel density estimation of the probability density functions. We test our algorithm on simulated data as well as airborne and spaceborne SAR data over sea ice. For the simulated cases, average per-class classification accuracy is improved between 0.5% and 4% compared to traditional all-at-once classification. Classification accuracy for the airborne and spaceborne SAR datasets was improved by 2.5% and 1%, respectively. In all cases, individual classes can show larger improvements up to 8%. Furthermore, the selection of individual feature sets for each single class can provide additional insights into physical interpretation of different features. The improvement in classification results comes at the cost of longer computation time, in particular during the design and training stage. The final choice of the optimal algorithm therefore depends on time constraints and application purpose

Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification

Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate the inclusion of Sentinel-1 (S1) texture features into a Bayesian classifier that accounts for linear per-class variation of its features with IA. We use the S1 extra-wide swath (EW) product in ground-range detected format at medium resolution (GRDM), and we compute seven grey level co-occurrence matrix (GLCM) texture features from the HH and the HV backscatter intensity in the linear and logarithmic domain. While GLCM texture features obtained in the linear domain vary significantly with IA, the features computed from the logarithmic intensity do not depend on IA or reveal only a weak, approximately linear dependency. They can therefore be directly included in the IA-sensitive classifier that assumes a linear variation. The different number of looks in the first sub-swath (EW1) of the product causes a distinct offset in texture at the sub-swath boundary between EW1 and the second sub-swath (EW2). This offset must be considered when using texture in classification; we demonstrate a manual correction for the example of GLCM contrast. Based on the Jeffries–Matusita distance between class histograms, we perform a separability analysis for 57 different GLCM parameter settings. We select a suitable combination of features for the ice classes in our data set and classify several test images using a combination of intensity and texture features. We compare the results to a classifier using only intensity. Particular improvements are achieved for the generalized separation of ice and water, as well as the classification of young ice and multi-year ice

Alignment of Multifrequency SAR Images Acquired over Sea Ice Using Drift Compensation

In this article, we investigate the feasibility to align synthetic aperture radar (SAR) imagery based on a compensation for sea ice drift occurring between temporally shifted image acquisitions. The image alignment is a requirement for improving sea ice classification by combining multifrequency SAR images acquired at different times. Images obtained at different radar frequencies provide complementary information, thus reducing ambiguities in the separation of ice types and the retrieval of sea ice parameters. For the alignment we use ice displacement vectors obtained from a sea ice drift retrieval algorithm based on pattern matching. The displacement vectors are organized on a triangular mesh and used for a piecewise affine transformation of the slave image onto the master image. In our case study, we developed an alignment framework for pairs of Advanced Land Observing Satellite-2 PALSAR-2 (L-band) and Sentinel-1 (C-band) images. We demonstrate several successful examples of alignment for time gaps ranging from a few hours to several days, depending on the ice conditions. The data were acquired over three test sites in the Arctic: 1) Belgica Bank, 2) Fram Strait, and 3) Lincoln Sea. We assess the quality of the alignment using the structural similarity index. From the displacement vectors, locations and extensions of patches of strong ice deformation are determined, which allows to estimate the possible areal size of successful alignment over undeformed ice and a judgment of the expected quality for each image pair. The comprehensive assessment of hundreds of aligned L–C SAR pairs shows the potential of our method to work under various environmental conditions provided that the ice drift can be estimated reliably