We examine the performance of an automated sea ice classification algorithm based on TerraSAR-X ScanSAR data. In the first step of our process chain, gray-level co-occurrence matrix(GLCM)-based texture features are extracted from the image. In the second step, these data are fed into an artificial neural network to classify each pixel. Performance of our implementation is examined by utilizing a time series of ScanSAR images in the Western Barents Sea, acquired in spring 2013. The network is trained on the initial image of the time series and then applied to subsequent images. We obtain a reasonable classification accuracy of at least 70% depending on the choice of our ice-type regime, when the incidence angle range of the training data matches that of the classified image. Computational cost of our approach is sufficiently moderate to consider this classification procedure a promising step toward operational, near-realtime ice charting.Index Terms-Earth and atmospheric sciences, pattern analysis, remote sensing, texture.

Satellite-borne synthetic aperture radar has proven to be a valuable tool for sea ice monitoring for more than two decades. In this study, we examine the performance of an automated sea ice classification algorithm based on polarimetric TerraSAR-X images. In the first step of our approach, we extract 12 polarimetric features from HH-VV dualpol StripMap images. In a second step, we train an artificial neural network, and then, feed the feature vectors into the trained neural network to classify each pixel into an ice type. The first part of our analysis addresses the predictive value of different subsets of features for our classification process (by means of measuring mutual information). Some polarimetric features such as polarimetric span and geometric intensity are proven to be more useful than eigenvalue decomposition based features. The classification is based on and validated by in situ data acquired during the N-ICE2015 field campaign. The results on a TerraSAR-X dataset indicate a high reliability of a neural network classifier based on polarimetric features. Performance speed and accuracy promise applicability for near real-time operational use.

This work compares the polarimetric backscatter behavior of sea ice in spaceborne X-band and C-band Synthetic Aperture Radar (SAR) imagery. Two spatially and temporally coincident pairs of fully polarimetric acquisitions from the TerraSAR-X/TanDEM-X and RADARSAT-2 satellites are investigated. Proposed supervised classification algorithm consists of two steps: The first step comprises a feature extraction, the results of which are ingested into a neural network classifier in the second step. Based on the common coherency and covariance matrix, we extract a number of features and analyze the relevance and redundancy by means of mutual information for the purpose of sea ice classification. Coherency matrix based features which require an eigendecomposition are found to be either of low relevance or redundant to other covariance matrix based features, which makes coherency matrix based features dispensable for the purpose of sea ice classification. Among the most useful features for classification are matrix invariant based features (Geometric Intensity, Scattering Diversity, Surface Scattering Fraction). This analysis reveals analogous results for all four acquisitions, in both X-band and C-band frequencies. The subsequent classification produces similarly promising results for all four acquisitions. In particular, the overlapping image portions exhibit a reasonable congruence of detected ice types.

Synthetic Aperture Radar (SAR) polarimetry has become a valuable tool in space-borne SAR-based sea ice analysis. The two major objectives in SAR-based remote sensing of sea ice are, on the one hand, to have a large coverage and, on the other hand, to obtain a radar response that carries as much information as possible in order to characterize sea ice. Single-polarimetric acquisitions of existing sensors offer a wide coverage on the ground, whereas dual polarimetric or even better fully polarimetric data offer a higher information content, which allows for a more reliable automated sea ice analysis at a cost of smaller swath. In order to reconcile the advantages of fully polarimetric acquisitions with the higher ground coverage of acquisitions with fewer polarimetric channels, hybrid/compact polarimetric acquisitions offer an excellent tradeoff between the mentioned objectives. With the advent of the RISAT-1 satellite platform, we are able to explore the potential of compact dual pol acquisitions for sea ice analysis and classification. Our algorithmic approach for an automated sea ice classification consist of two steps. In the first step, we perform a feature extraction followed by a feature evaluation procedure. The resulting feature vectors are then ingested into a trained artificial neural network classifier to arrive at a pixel-wise supervised classification. We present a comprehensive polarimetric feature analysis and classification results on a dataset acquired off the eastern Greenland coast, along with comparisons of results obtained from near-coincident (spatially and temporally) C-band fully polarimetric imagery acquired by RADARSAT-2.

In northern latitudes, icebergs frequently cross shipping routes and impair marine traffic. To improve ship routing, we explore the capabilities of an algorithm that detects and charts icebergs from images provided by the German radar satellite TerraSAR-X. TerraSAR-X is in a near-polar orbit, equipped with an active X-Band radar antenna and, thus, allows monitoring the ocean and frozen waters regardless of cloud cover and darkness. The algorithm we apply is based on the iterative censoring constant false alarm rate (IC-CFAR) detector, which has proven its usefulness for terrestrial target detection already. Unlike the standard approach, we not only estimate statistical properties of open water intensities expressed by a probability density function, but also search for recurring patterns (i.e., waves). This allows discriminating icebergs from most false alarms that arise from rough sea and strong winds. Experiments carried out with a series of HH-polarized TerraSAR-X Stripmap images acquired between 2012 and 2015 confirm that, due to consideration of wave pattern during image processing, the false alarm rate is reduced by a factor of 3. Résumé. Dans les régions de haute latitude, les icebergs croisent régulièrement les routes maritimes, gênant ainsi le trafic en mer. Afin d'améliorer le routage des navires, nousétudions un algorithme qui détecte et cartographie les icebergsà partir d'images radar du satellite allemand TerraSAR-X. TerraSAR-X est sur une orbite quasi-polaire. Il estéquipé d'une antenne radar active en bande X, ce qui lui permet d'observer les océans et eaux glacées de jour comme de nuit et ce, quelle que soit la couverture nuageuse. L'algorithme que nous utilisons est basé sur le détecteur IC-CFAR qui a déjà prouvé son utilité pour la détection de cibles terrestres. Contrairementà l'approche habituelle, nous n'évaluons pas seulement les propriétés statistiques des intensités des eaux libres exprimées par une fonction de densité de probabilité, mais nous recherchonségalement des motifs récurrents (par exemple des vagues). Ceci permet de distinguer les icebergs de la plupart des fausses alertes dues aux mers agitées et aux forts vents. Les essais effectués sur une série d'images Stripmapà polarisation HH du satelliteTerraSAR-X prises entre 2012 et 2015 confirment que le taux de fausses alertes est divisé par 3 grâceà la prise en compte des motifs des vagues pendant le traitement.