I checked out the publications of the competition hosts. This is a good sized group trying this and that AI for years without getting anywhere, hence the scheme to get outside groups to do it for them. [[see Chap2 Adventures of Tom Sawyer]]
It may prove worthwhile to skim through the papers looking for approaches to avoid such as convolutional neural networks. Also they identify the commercial 'stakeholder' who gets to 'view' your code under the yet-to-be-described NDA.
It would be highly amusing if you can win the contest but refuse to give them the code. Or give them code that instead deletes the 80ºN dmi graphic. To make up for no prize, I can offer a link to a ImageJ tutorial valued at the same €9000.
Interpolation of AMSR2 data for improvement of ice charting
AA Nielsen, R Saldo, Jørgen Buus-Hinkler, MB Kreiner
https://backend.orbit.dtu.dk/ws/portalfiles/portal/197991141/imm7139.pdf Today, ice charts in Greenland waters are produced manually by the Danish Meteorological Institute (DMI) for selected regions depending on season and shipping routes. The project “Automated Downstream Sea Ice Products for Greenland Waters” or shorter “Automated Sea Ice Products” (ASIP) attempts to automate this process by means of fusion of data from instruments with different resolutions and modalities.
As a part of this process data from the Advanced Microwave Scanning Radiometer (AMSR2) will be interpolated to the geometry of the SAR data acquired by Sentinel-1. In a preparatory leave-one-out cross-validation (LOOCV) study, different interpolation methods including ordinary kriging (OK) are compared.
Using bias and root-mean-squared error (RMSE) as measures of precision, OK using 20-30 nearest neighbors outperforms other often used methods such as inverse distance (ID) weighting. This comes at a cost: more work needs to be done by both the operator and the computer.
A Convolutional Neural Network Architecture for Sentinel-1 and AMSR2 Data Fusion
https://ieeexplore.ieee.org/abstract/document/9133205/ July 2020
With a growing number of different satellite sensors, data fusion offers great potential in many applications. In this work, a convolutional neural network (CNN) architecture is presented for fusing Sentinel-1 synthetic aperture radar (SAR) imagery and the Advanced Microwave Scanning Radiometer 2 (AMSR2) data. The CNN is applied to the prediction of Arctic sea ice for marine navigation and as input to sea ice forecast models. This generic model is specifically well suited for fusing data sources where the ground resolutions of the sensors differ with orders of magnitude, here 35 km × 62 km (for AMSR2, 6.9 GHz) compared with the 93 m × 87 m (for sentinel-1 IW mode).
In this work, two optimization approaches are compared using the categorical cross-entropy error function in the specific application of CNN training on sea ice charts. In the first approach, concentrations are thresholded to be encoded in a standard binary fashion, and in the second approach, concentrations are used as the target probability directly. The second method leads to a significant improvement in R 2 measured on the prediction of ice concentrations evaluated over the test set.
The performance improves both in terms of robustness to noise and alignment with mean concentrations from ice analysts in the validation data, and an R2 value of 0.89 is achieved over the independent test set. It can be concluded that CNNs are suitable for multisensor fusion even with sensors that differ in resolutions by large factors, such as in the case of Sentinel-1 SAR and AMSR2.
Fusion of satellite SAR and passive microwave radiometer data for automated sea ice mapping and the expected impact of CIMR observations
Same authors
https://orbit.dtu.dk/en/publications/fusion-of-satellite-sar-and-passive-microwave-radiometer-data-for 2021
https://orbit.dtu.dk/en/publications/automatic-satellite-based-ice-charting-using-ai 2019
Manual ice charting from multi-sensor satellite data analysis has for many years been the primary method at the National Ice Services for producing sea ice information for marine safety. Ice analysts primarily use satellite synthetic aperture radar (SAR) imagery due to the high spatial resolution and the capability to image the surface through clouds and in polar darkness, but also optical imagery in clear sky and daylight conditions, thermal-infrared and microwave radiometer data from e.g. AMSR2.
Ice analysts mention the spatial resolution of microwave radiometers as the primarily limitation to use the data. The traditional manual ice charting method is time-consuming and limited in spatial and temporal coverage. Further, it is challenged by an increasing amount of available satellite imagery, along with a growing number of users accessing wider parts of the Arctic due to the thinning of the Arctic sea ice. The automation of the time-consuming and labour-intensive sea ice charting process has potential to provide users with near-real time sea ice products of higher spatial resolution, larger spatial and temporal coverage, and increased consistency.
To automate the generation of sea ice information from satellite imagery we use a Convolutional Neural Network (CNN) designed for prediction of sea ice in Greenland waters. Automating the process on SAR data alone is challenging. SAR images show patterns related to ice formations, but backscatter intensities can be ambiguous, which complicates the discrimination between ice and open water, e.g. at high wind speeds. Our CNN model tackles the challenges by fusing Sentinel-1 active microwave (SAR) data with Microwave Radiometer (MWR) data from AMSR2 to exploit the advantages of both instruments.
While SAR data has ambiguities, it has a very high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. The CNN model has been trained with a large dataset of 461 ice charts manually produced by the ice analysts in the DMI Greenland Ice Service based on Sentinel-1 imagery.
The dataset also contains the corresponding AMSR2 swath co-located with the ice charts and Sentinel-1 images. The sea ice training dataset has been co-produced in the ASIP and AI4Arctic (ESA) projects. We will present the results of merging active and passive microwave data from Sentinel-1 and AMSR2 as input to a CNN and show how the input from the passive microwave data has a positive effect on the CNN performance.
https://doi.org/10.11583/DTU.13011134.v2In this work we explore data fusion and image segmentation techniques with Convolutional Neural Networks to produce per pixel predictions from Sentinel 1 (S1) SAR images and AMSR2 microwave radiometer measurements of Ice/water. The work is carried out under the Danish Automated Sea Ice Products (ASIP) project in a collaboration between the Danish Meteorological Institute and the Technical University of Denmark.
For the study a dataset of more than 900 ice charts and corresponding Sentinel1 SAR imagery has been collected. The core of our algorithm consists of a Convolutional Neural Network that models image features at different scales by the use of dilated convolutional filters. The architecture of the algorithm further allows us to merge S1 images with AMSR2 measurements in a data fusion approach that exploits the best properties of each measurement. While the 40m pixel size in Sentinel1 data enables extraction of ice information at an unprecedented high resolution, the AMSR2 measurements contributes with a high contrast between ice and water independent of wind conditions.
Future studies in the project will investigate the importance of additional meta data in the ice prediction, such as weather information, sensor viewing angles, geographic location, etc.
AI4SeaIce: Toward Solving Ambiguous SAR Textures in Convolutional Neural Networks for Automatic Sea Ice Concentration Charting
https://ieeexplore.ieee.org/abstract/document/9705586 Feb 2022
Automatically producing Arctic sea ice charts from Sentinel-1 synthetic aperture radar (SAR) images is challenging for convolutional neural networks (CNNs) due to ambiguous backscattering signatures. The number of pixels viewed by the CNN model in the input image used to generate an output pixel, or the receptive field, is important to detect large features or physical objects such as sea ice and correctly classify them. In addition, a noise phenomenon is present in the Sentinel-1 ESA Instrument Processing Facility (IPF) v2.9 SAR data, particularly in subswath transitions, visible as long vertical lines and grained particles resembling small sea ice floes.
To overcome these two challenges, we suggest adjusting the receptive field of the popular U-Net CNN architecture used for semantic segmentation. It is achieved by symmetrically adding additional blocks of convolutional, pooling and upsampling layers in the encoder and decoder of the U-Net, constituting an increase in the number of levels. This shows great improvements in the performance and in the homogeneity of predictions.
Second, training models on SAR data noise-corrected with an enhanced technique has demonstrated a significant increase in model performance and enabled better predictions in uncertain regions. An eight-level U-Net trained on the alternative noise-corrected SAR data is presented to be capable of correctly predicting many ambiguous SAR signatures and increased the performance by 8.44% points compared with the regular U-Net trained on the ordinary ESA IPF v2.9 noise-corrected SAR data. This is the first installment of this multi-series installment of articles related to AI applied to sea ice (in short AI4SeaIce).
High-Resolution Sea Ice Maps with Convolutional Neural Networks
http://www2.compute.dtu.dk/pubdb/pubs/7133-full.html 2019 conf
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 Arctic 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. 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.
http://harnvig-ice.dk/This resulted in ASIP Internal Stakeholder Survey Report which substantiates the specific 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 theonly source with regularly coverage as input data.
Iceberg Detection in Dual-Polarized C-Band SAR Imagery by Segmentation and Nonparametric CFAR (SnP-CFAR)
https://ieeexplore.ieee.org/abstract/document/9406184 April 2021
We propose an unsupervised method for iceberg detection over sea ice-free waters. The algorithm is based on the segmentation and nonparametric constant false alarm rate (SnP-CFAR) approach. Unlike in parametric CFAR detection, in our method, there is no need to define target, guard, and background areas explicitly. Instead, we apply the CFAR detection to the pixels within each detected segment and the background is formed of the nearby pixels not included in the target segment.
By using nonparametric background probability density function (PDF) estimates, we also eliminate the need of assuming a specific type of a background PDF. We compared the detection results with the operational Danish Meteorological Institute (DMI) Gamma-CFAR algorithm results. The results were evaluated against icebergs manually identified by the Finnish Meteorological Institute (FMI) Ice analysts.
Our method also exhibits a reduced number of false alarms. We present results of iceberg detection based on the SAR channel-cross-correlation (CCC). CCC was able to distinguish many of the true targets with a low number of false alarms. However, CCC seems to
miss some of the true targets and its main use would be in confirming iceberg observations.
Field tracking (GPS) of ten icebergs in eastern Baffin Bay, offshore Upernavik, northwest Greenland
https://tinyurl.com/2zdjsmyf 2017
A field investigation of iceberg drift pattern and drift speed was conducted in September 2011 in Baffin Bay, northwest Greenland. Ten icebergs were equipped with GPS transponders during a field campaign. Above-waterline dimensions (length, width and height) of the icebergs were measured using a GPS/pressure altimeter and geometrically rectified digital photographs taken during the field campaign. Iceberg lengths, masses and drafts ranged from 95 to 450 m, 330 000 to 17 000 000 t and 70 to 260 m, respectively. The drift patterns and speeds were determined on the basis of GPS positions logged continuously at 1 hour intervals.
The drift patterns differed significantly from iceberg to iceberg. The GPS signal was
lost on six of the icebergs
within the first 23 days of logging. Three transponders were transferring data for more than 5 months until the battery ran out of power. One transponder was sending data until summer 2012. The measured maximum drift speed was 68 cm s−1 (2.4 km h−1), and the mean drift speed for all ten icebergs was 10 cm s−1 (0.4 km h−1). Relations between iceberg size and drift speed were investigated, showing that icebergs with large surface areas moved at the highest speeds, which occurred particularly during strong wind conditions.
Jørgen Buus-Hinkler received the Ph.D. degree from the University of Copenhagen, Copenhagen, Denmark, in
2005, focusing on snow-precipitation in Northeast Greenland and its relation to sea-ice distribution gathered from passive microwave imagery. He has been working as a Research Scientist at the Danish Meteorological Institute, Helsinki, Finland the fields of remote sensing and geospatial analysis. Part of his present work is the development of operational iceberg products based on target detection in SAR imagery. This work is within the Copernicus Marine Environment Monitoring Service (CMEMS).
