Classification of Sea Ice Types in ENVISAT Synthetic Aperture Radar Images

Zakhvatkina, Natalia; Alexandrov, V.; Johannessen, Ola M.; Sandven, Stein; Frolov, I. Ye.

doi:10.1109/tgrs.2012.2212445

Cited by 153 publications

(138 citation statements)

References 34 publications

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“…The intensity contrast between HH and HV polarization of open water increases at higher wind speeds, and open water is distinguished more reliably on dual-polarized RS2 data. Previous studies of ENVISAT ASAR HH data in wide swath mode showed a similar backscatter dependence on incidence angle (Zakhvatkina et al, 2013), and the same tech-nique was applied for the HH band of RS2 SCW images. The ScanSAR image swath consists of different combinations of four physical beams and there are well-known technical features caused by a wave-like modulation of the image intensity in range direction throughout the entire image in the sub-swaths and their edges of HV band (Romeiser et al, 2013).…”

Section: Resultsmentioning

confidence: 87%

“…The coefficient was defined by calculating the linear trend of the observed backscatter signal on several HH-polarized RS2 SCW images of pack ice. The procedure is similar to the pre-processing of ENVISAT ASAR data in Zakhvatkina et al (2013). The backscatter normalization to a pre-defined incidence angle provides homogenous image contrast across the swath over ice-covered areas.…”

Section: Incidence Angle Correction For Hhmentioning

confidence: 99%

“…Including more texture features for both channels was tested but found not to improve the information content. The calculation parameters were found experimentally to give a good compromise between speckle noise reduction, preservation of details, and correct classification results (methodology description in Zakhvatkina et al, 2013).…”

Section: Texture Feature Calculationmentioning

confidence: 99%

“…A neural-network-based algorithm has been developed for ENVISAT SAR images for operational sea ice classification including validation (Zakhvatkina et al, 2013). The algorithm discriminated the level FYI, deformed FYI, MYI, and open water/nilas in the high Arctic in winter conditions and demonstrated good applicability in the central Arctic.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Zakhvatkina

Korosov²,

Muckenhuber³

et al. 2017

The Cryosphere

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Abstract. Synthetic Aperture Radar (SAR) data from RADARSAT-2 (RS2) in dual-polarization mode provide additional information for discriminating sea ice and open water compared to single-polarization data. We have developed an automatic algorithm based on dual-polarized RS2 SAR images to distinguish open water (rough and calm) and sea ice. Several technical issues inherent in RS2 data were solved in the pre-processing stage, including thermal noise reduction in HV polarization and correction of angular backscatter dependency in HH polarization. Texture features were explored and used in addition to supervised image classification based on the support vector machines (SVM) approach. The study was conducted in the ice-covered area between Greenland and Franz Josef Land. The algorithm has been trained using 24 RS2 scenes acquired in winter months in 2011 and 2012, and the results were validated against manually derived ice charts of the Norwegian Meteorological Institute. The algorithm was applied on a total of 2705 RS2 scenes obtained from 2013 to 2015, and the validation results showed that the average classification accuracy was 91 ± 4 %.

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Section: Resultsmentioning

confidence: 87%

Section: Incidence Angle Correction For Hhmentioning

confidence: 99%

Section: Texture Feature Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Zakhvatkina

Korosov²,

Muckenhuber³

et al. 2017

The Cryosphere

Self Cite

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show abstract

“…In the case of supervised classification, finding or fitting such a functional relationship is referred to as training. Such generic methods have been tested in various flavors for sea ice classification successfully (Tsatsoulis 2004, Bogdanov 2005, Breivik, 2012, Zakhvatkina 2013, Clausi 2002, Clausi 2004, Scheuchl 2003, Ressel 2014). …”

Section: Basic Conceptmentioning

confidence: 99%

Navigation Assistance for Ice-Infested Waters Through Automatic Iceberg Detection and Ice Classification Based on Terrasar-X Imagery

Ressel

Frost

Lehner

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Most icebergs present in northern latitudes originate from western Greenland glaciers, from where they drift into Baffin Bay, circulating north along Greenland coast and south along Canadian coast. Some of them drift more southwards up to Newfoundland, where they frequently cross shipping routes. Furthermore, the Arctic summer sea ice coverage significantly decreased over the last three decades. This has attracted numerous attentions from maritime end-users. To keep Arctic shipping routes safe, the monitoring of sea ice and icebergs is crucial. For this purpose, satellite-based Synthetic Aperture Radar (SAR) is well suited. Equipped with an active radar antenna, SAR satellites provide image data of the ocean and frozen waters independent of weather conditions, cloud cover or absence of daylight. In this paper, we present a processor for sea ice classification and (subsequent) iceberg detection based on TerraSAR-X imagery. In the classification step, texture features are extracted from the images and fed into a neural network, indicating areas of low sea ice concentration. Then, an adapted Constant False Alarm Rate (CFAR) detector is executed in order to detect icebergs. In the end, sea ice boundary and iceberg positions are output. Our experiments deal with HH polarized TerraSAR-X images taken in spring season in the Baffin Bay off the western Greenland coast, where both, sea ice and icebergs are present. Our results exemplify how a comprehensive ice processor with complementary information can be set up for near real time (NRT) service in ice infested waters.

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Prediction of the Lotus Effect on Solid Surfaces by Machine Learning

Zhang

Xiong

et al. 2022

Small

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Superhydrophobic surfaces with the “lotus effect” have wide applications in daily life and industry, such as self‐cleaning, anti‐freezing, and anti‐corrosion. However, it is difficult to reliably predict whether a designed superhydrophobic surface has the “lotus effect” by traditional theoretical models due to complex surface topographies. Here, a reliable machine learning (ML) model to accurately predict the “lotus effect” of solid surfaces by designing a set of descriptors about nano‐scale roughness and micro‐scale topographies in addition to the surface hydrophobic modification is demonstrated. Geometrical and mathematical descriptors combined with gray level cooccurrence matrices (GLCM) offer a feasible solution to the puzzle of accurate descriptions of complex topographies. Furthermore, the “black box” is opened by feature importance and Shapley‐additive‐explanations (SHAP) analysis to extract waterdrop adhesion trends on superhydrophobic surfaces. The accurate prediction on as‐fabricated superhydrophobic surfaces strongly affirms the extensionality of the ML model. This approach can be easily generalized to screen solid surfaces with other properties.

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Classification of Sea Ice Types in ENVISAT Synthetic Aperture Radar Images

Cited by 153 publications

References 34 publications

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Operational algorithm for ice–water classification on dual-polarized RADARSAT-2 images

Navigation Assistance for Ice-Infested Waters Through Automatic Iceberg Detection and Ice Classification Based on Terrasar-X Imagery

Prediction of the Lotus Effect on Solid Surfaces by Machine Learning

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