This work extends upon our simple feature-based multichannel SAR segmentation method to incorporate highly desirable statistical properties into a computationally simple approach. The desirable properties include Markov random field contextual smoothing and goodness-of-fit testing to automatically obtain the significant number of classes. To achieve this we need to find an explicit class model to ...

In this paper, we further analyse the problem that polarimetric target decomposition methods in general have more physical parameters than equations, making the decomposition under-determined and hence have no unique solution. The common approach to get around this problem is to make certain assumptions, thus fixing one or more parameters, allowing the other free parameters to be solved from the set ...

In this paper, we introduce a new approach for investigation of polarimetric Synthetic Aperture Radar (PolSAR) images for oil slick analysis. Our method aims at enhancing discrimination of oil types by exploring the polarimetric features that can be produced by processing PolSAR scenes without dimensionality reduction. Taking advantage of a mixture description of the interactions among classes within ...

In this paper, we present an unsupervised change detection method for polarimetric synthetic aperture radar (PolSAR) images based on the relaxed Wishart distribution. Most polarimetric change detectors assume the Gaussian-based complex Wishart model for multilook covariance matrices, which is only satisfied for homogeneous areas with fully developed speckle and no texture. Liu et al. recently proposed ...

In this paper we investigate the performance of an algorithm for automatic segmentation of full polarimetric, synthetic aperture radar (SAR) sea ice scenes. The algorithm uses statistical and polarimetric properties of the backscattered radar signals to segment the SAR image into a specified number of classes. This number was determined in advance from visual inspection of the SAR image and by ...

This letter discusses cross-correlation features de- rived from near-coincident RADARSAT-2 quad-polarimetric and RISAT-1 hybrid-polarity (HP) measurements collected during the NOrwegian Radar oil Spill Experiment in 2015 (NORSE2015). We show that the imaginary part of the cross-correlation between RH and RV is an HP parallel to the real part of the cross-correlation between HH and VV earlier ...

In this article, we propose a novelteacher–student-based label propagation deep semisupervised learning (TSLP-SSL) method for sea ice classification based on Sentinel-1 synthetic aperture radar data. For sea ice classification, labeling the data precisely is very time consuming and requires expert knowledge. Our method efficiently learns sea ice characteristics from a limited number of labeled samples ...

Bringing together a number of cutting-edge technologies that range from storing extremely large volumes of data all the way to developing scalable machine learning and deep learning algorithms in a distributed manner and having them operate over the same infrastructure poses unprecedented challenges. One of these challenges is the integration of European Space Agency (ESA)’s Thematic Exploitation ...

This paper will investigate how new developments in remote sensing and sensor technologies can be applied to image the structure of the sea ice surface. Both segmentation of multipolarimetric synthetic aperture radar images and strategies for the analyses of polarimetric SAR data of sea ice are addressed. The analysis is based on a Radarsat 2 PolSAR scene of from the Fram Strait in September ...

We apply a polarimetric two-scale model to multifrequency synthetic aperture radar imagery of verified oil slicks measured by DLRs F-SAR instrument, which can acquire high spatial resolution and high signal-to-noise data. The purpose, is to determine the permittivity of the scattering surface via an inversion procedure. The ocean surface is modelled as an ensemble of randomly orientated, tilted ...