Automated SEA ICE Classification Over the Baltic SEA using Multiparametric Features of Tandem-X Insar Images

In this study, bistatic interferometric Synthetic Aperture Radar (InSAR) data acquired by the TanDEM-X mission were used for automated classification of sea ice over the Baltic Sea, in the Bothnic Bay. A scene acquired in March of 2012 was used in the study. Backscatter-intensity, coherence-magnitude and InSAR-phase, as well as their different combinations, were used as informative features in several classification approaches. In order to achieve the best discrimination between open water and several sea ice types (new ice, thin smooth ice, close ice, very close ice, ridged ice, heavily ridged ice and ship-track), Random Forests (RF) and Maximum likelihood (ML) classifiers were employed. The best overall accuracies were achieved using combination of backscatter-intensity & InSAR-phase and backscatter-intensity & coherence-magnitude, and were 76.86% and 75.81% with RF and ML classifiers, respectively. Overall, the combination of backscatter-intensity & InSAR-phase with RF classifier was suggested due to the highest overall accuracy (OA) and smaller computing time in comparison to ML. In contrast to several earlier studies, we were able to discriminate water and the thin smooth ice.Peer reviewe

Remote sensing of sea ice properties and dynamics using SAR interferometry

Landfast ice is attached to the coastline and islands and stays immobile over most of the ice season. It is an important element of polar ecosystems and plays a vital role as a marine habitat and in life of local people and economy through offshore technology. Landfast ice is routinely used for on-ice traffic, tourism, and industry, and it protects coasts from storms in winter from erosion. However, landfast ice can break or experience deformation in order of centimeters to meters, which can be dangerous for the coastline and man-made structures, beacons, on-ice traffic, and represents a safety risk for working on the ice and local people. Therefore, landfast ice deformation and stability are important topics in coastal engineering and sea ice modeling. In the framework of this dissertation, InSAR (SAR Interferometry) technology has been applied for deriving landfast ice displacements (publication I), and mapping sea ice morphology, topography and its temporal change (publication III). Also, advantages of InSAR remote sensing in sea ice classification compared to backscatter intensity were demonstrated (publications II and IV). In publication I, for the first time, Sentinel-1 repeat-pass InSAR data acquired over the landfast ice areas were used to study the landfast ice displacements in the Gulf of Bothnia. An InSAR pair with a temporal baseline of 12 days acquired in February 2015 was used. In the study, the surface of landfast ice was stable enough to preserve coherence over the 12-day period, enabling analysis of the interferogram. The advantage of this long temporal baseline is in separating the landfast ice from drift ice and detecting long-term trends in deformation maps. The interferogram showed displacements of landfast ice on the order of 40 cm. The main factor seemed to be compression by drift ice, which was driven against the landfast ice boundary by strong winds from southwest. Landfast ice ridges can hinder ship navigation, but grounded ridges help to stabilize the ice cover. In publication III, ridge formation and displacements in the landfast ice near Utqiaġvik, Alaska were examined. The phase signatures of two single-pass bistatic X-band SAR (Synthetic Aperture Radar) image pairs acquired by TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) satellite on 13 and 24 January 2012 were analyzed. Altogether six cases were identified with ridge displacement in four and formation in two cases under onshore compression. The ridges moved approximately 0.6 and 3.7 km over the study area and ridge formation reached up to 1 meter in upward. The results well corresponded with the locations identified as convergence zones retrieved from the drift algorithm generated by a SAR-based sea ice-tracking algorithm, backscatter intensity images and coastal radar imagery. This method could potentially be used in future to evaluate sea ice stability and ridge formation. A bistatic InSAR pair acquired by the TanDEM-X mission in March 2012 over the Bothnian Bay was used in two further studies (publications II and IV). The potential of X-band InSAR imagery for automated sea ice classification was evaluated. The first results were presented in publication II and the data were further elaborated in publication IV. The backscatter intensity, coherence magnitude and InSAR-phase features, as well as their different combinations, were used as the informative features in classification experiments. In publication II, the purpose was to assess ice properties on the scale used in ice charting, with ice types based on ice concentration and sea ice morphology, while in publication IV, a detailed small-scale analysis was performed. In addition, the sampling design was different in these publications. In publication II, to achieve the best discrimination between open water and several sea-ice types, RF (Random Forests) and ML (Maximum likelihood) classifiers were employed. The best overall accuracies were achieved by combining backscatter intensity & InSAR-phase using RF approach and backscatter intensity & coherence-magnitude using ML approach. The results showed the advantage of adding InSAR features to backscatter intensity for sea ice classification. In the further study (publication IV), a set of state-of-the-art classification approaches including ML, RF and SVM (Support Vector Machine) classifiers were used to achieve the best discrimination between open water and several sea-ice types. Adding InSAR-phase and coherence magnitude to backscatter intensity improved the OA (Overall Accuracy) compared to using only backscatter intensity. The RF and SVM algorithms gave somewhat larger OA compared to ML at the expense of a somewhat longer processing time. Results of publications II and IV demonstrate InSAR features have potential to improve sea ice classification. InSAR could be used by operational ice services to improve mapping accuracy of automated sea ice charting with statistical and machine learning classification approaches.Viime vuosikymmeninä satelliittivälitteisestä SAR-tutkasta on tullut erittäin tärkeä työkalu merijään kaukokartoituksessa. Tämän tutka perustuu sähkömagneettisten aaltojen sirontaan kiinnostavasta kohteesta takaisin tutkaan, mitä seuraa signaalin voimakkuuden mittaaminen. SAR-tutkat käyttävät synteettistä antennia, joka perustuu satelliitin liikkeeseen, mikä mahdollistaa tarkkojen, korkean erotuskyvyn kuvien tuottamisen. SAR-anturit mittaavat myös signaalin vaihetta, jota käytetään interferometria tekniikassa pinnan topografian ja siirtymien laskemiseen eri sovelluksissa, kuten maan muodonmuutoksissa, tarkassa kartoituksessa, maanjäristyksen arvioinnissa ja tulivuorenpurkauksien tarkkailussa. Interferometri tekniikkaa käytettiin tässä opinnäytetyössä pienten jäänsiirtymien analysointiin kiintojäävyöhykkeellä, joka on kiinni rantaviivassa ja saarissa eikä juuri liiku tuulien tai virtausten mukana. Kiintojääalueilla on pohjaan tarttuneita jäävalleja, jotka edistävät kiintojääpeitteen vakautumista. Kiintojäällä on tärkeä rooli merellisenä elinympäristönä, maankäytön kysymyksissä sekä paikallisten ihmisten elämässä ja meritekniikassa. Kiintojää voi murtua liikahdella useita metrejä, mikä voi olla vaarallista rakenteille, majakoille ja jäällä liikkujille. Tässä väitöskirjassa Sentinel-1A ja TanDEM-X satelliitteja ja interferometri tekniikkaa on käytetty arktisilla alueilla ja Itämerellä mittaamaan kiintojään muodonmuutoksia ja siirtymiä sekä niihin liittyviä mekanismeja. Lisäksi on tutkittu automaattista merijääluokitusta interferometrian apuohjelmiston avulla, mikä laajentaa operatiivisten merijääpalvelujen tutkahavaintojen käyttöä. Sentinel-1A:n avulla voitiin tarkastella 12 päivän pituisia muutoksia Pohjanlahden kiintojäävyöhykkeellä, kun interferometria tekniikka mittasi voimakkaan tuulen aiheuttaman 40 cm:n siirtymiä. Pohjoisella jäämerellä voitiin tunnistaa jäävallien siirtymiä ja muodostumia. Vallit siirtyivät noin 0,6 ja 3,7 km matkoja ja muodostuessaan ne kasvoivat metrin korkeuteen. Interferometri tekniikan lisääminen tutkakuvauksen analyysiin osoitti potentiaalin parantaa automaattisen merijääkartoituksen kartoituksen tarkkuutta tilastollisilla ja koneoppimiseen perustuvan luokittelun menetelmillä. Tulevaisuuden työnä merijään luokituksessa ja vallitutkimuksissa olisi suositeltavaa käyttää erilaisia ja useampia tutkakuvauksen geometrioita sekä erilaisia jääolosuhteita eri sääolosuhteiden vallitessa

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification performance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91% for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05% using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.Peer reviewe