High-Resolution Sea Ice Maps with Convolutional Neural Networks |
| | Abstract | Automatically generated high resolution sea ice maps have the potential to increase the use of satellite imagery in arctic applications. Applications include marine navigation, offshore operations, validation of ice models, and climate research. Especially for arctic marine navigation, frequent ice maps in high resolution are requested by most users, as documented by an internal project
stakeholder survey.
We present current results from our large-scale study of high resolution ice maps generation with Convolutional Neural Networks (CNNs). Our study is based on dual polarized (HH+HV) Extra Wide swath (EW) SAR data from the Copernicus Sentinel 1 satellite mission and we generate pixel-wise sea ice estimates in 40m x 40m resolution. The presentation will include a model validation against expert annotations of SAR images. In the near future we will expand our study to include AMSR2 Microwave Radiometer (MWR) data as input. The addition of MWR data can potentially solve the ambiguities in SAR data over open water, due to SAR backscatter variation at different wind conditions. Some CNN estimates are observed to confuse very homogeneous ice surfaces with similar backscatter open water scenarios, but results show a clear potential for this methodology.
Our work is carried out under a Danish research project named Automated downstream Sea Ice Products (ASIP). The project goal is to automate generation of sea ice information from satellite images. ASIP is a collaboration between the Danish Meteorological Institute (DMI), the Technical University of Denmark and Harnvig Artic and Maritime. It sets out to automate, partially or fully, the extraction of arctic sea ice information from satellite imagery. Today, ice mapping is mainly done manually by ice-experts at national Ice Centers around the world. The project goal will enable analyzing larger quantities of satellite data, for better utilization of the available Sentinel-1 images and for providing ice maps to users more frequently. Recent literature shows an increased interest in algorithms for extraction of sea ice information, [2, 4]. As a part of the ASIP project a thorough analysis of the need for ice information was carried out among users by Harnvig Arctic and Maritime. This resulted in "ASIP Internal Stakeholder Survey Report", which substantiates the specic needs. One of the conclusions from this report is that 90% of use cases need simple ice/no-ice information for marine route planning purposes in high resolution (< 250m pr. pixel). Meeting this resolution requirement is unfortunately not possible with current MWR data alone, though its properties are otherwise good for ice concentration estimations. Hence, SAR data is the
only source with regularly coverage as input data.
A training data set was prepared by selecting all regional Ice Charts that were manually produced by the ice analysts in the DMI Ice Service based primarily on Sentinel-1 SAR imagery. SAR imagery has proven very suitable for Arctic sea ice monitoring, due to the radar sensors capability to see through clouds and in polar darkness. The training data set consists of a match-up of more than 900 ice charts and corresponding SAR imagery covering the period from the availability of the Sentinel-1A sensor data in November 2014 and Sentinel-1B data from September 2016, until December 2017. The individual ice charts cover dierent areas (depending on users and season) but the data set spans all of Greenland coast, apart from the northernmost regions, and with denser
coverage in Southern Greenland.
The CNN architecture we use in this work is inspired by models from image segmentation [1]. We train it on 400 000 sub-images of 250x250 pixels cut out from Sentinel-1 scenes corresponding to more than 25 billion pixel samples. Labels for each pixel are taken from ice concentration parameter in the ice-charts. Our initial approach has simply been to threshold ice concentrations,
and consider <10% as open water and otherwise ice. This approach turns the problem into a pixelwise classication task, i.e. segmentation, and our CNN model produces a 250x250 segmentation mask for each input image. The way ice charts are produced combined with our threshold approach introduces an amount of mis-labelled pixels, but Deep Neural Networks (DNNs) are known to cope with "noisy" labels [3].
Current results can be seen in Figure 1, which shows the sub-images in a Sentinel-1B scene from June 2017 that matches with an DMI produced Ice Chart. It should be noted that labels are very coarse compared to the level of detail in a Sentinel-1 image. Despite this, the CNN still correctly classies the lead of water running into the ice in Figure 1 (marked with a redcircle).
Acknowledgements:
We would like to thank our funding partner Innovation Fund Denmark and the NVIDIA Corporation for supplying the GPU Cards used for training our model.
References
[1] Liang-Chieh Chen, George Papandreou, Florian Schro, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017.
[2] Juha Karvonen. Baltic sea ice concentration estimation using sentinel-1 SAR and AMSR2 microwave radiometer data. IEEE Transactions on Geoscience and Remote Sensing, 55(5):2871{2883, 2017.
[3] Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta. Revisiting unreasonable effectiveness of data in deep learning era. In Computer Vision (ICCV), 2017 IEEE International Conference on, pages 843{852. IEEE, 2017.
[4] Lei Wang, K Andrea Scott, and David A Clausi. Sea ice concentration estimation during freeze-up from SAR imagery using a convolutional neural network. Remote Sensing, 9(5):408, 2017. | | Type | Conference paper [Abstract] | | Conference | ESA Living Planet Symposium | | Editors | Milan, Italy | | Year | 2019 Month May | | BibTeX data | [bibtex] | | IMM Group(s) | Image Analysis & Computer Graphics |
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