Sea Ice Detection in Radarsat Imagery using Statistical Distributions R. S. Gill IICWG-II, Reykavik. DMI, Copenhagen.

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Sea Ice Detection in Radarsat Imagery using Statistical Distributions R. S. Gill IICWG-II, Reykavik. DMI, Copenhagen.

Recall: Ice analysts often have to make ‘best guess’ of the position of the ice edge and the ice concentration when interpreting SAR data. ‘Best guess’ is often based on their experience of the region and on historical information. Accuracy of ice information has a direct impact on vessel safety !

Why is life so difficult for the ice analysts and developers? -SAR signals over open water and sea ice region are ambiguous. OPERATIONS: It’s problem of SAR image interpretation. Determining the ice edge and ice concentration -in regions with low ice concentration, -strong surface wind conditions, -surface melting (seasonal) In the waters around Greenland ! DEVELOPMENTS: -All tools/products give results that are also ambiguous. -Even interpreting them is a problem for some ice analysts. - Manual interpretation of grey tone images helps. -PMR most reliable and simplest CURRENT STATUS: -Large gap between what the ice analysts need and what the tools/products can deliver. -Be realistic in expectations: For really ‘nasty’ images nothing will works !

To develop ‘easily interpretable’ tools/products that would aid ice analysts to discriminate between the different regions in a SAR image in an operational environment. CONTENT: 1. Proposed new algorithms 2. Test results 3. Conclusions What is our goal ?

A. Ice Edge detection using distributions -Gamma pdf (undergoing testing -limiting case of k-pdf), -k- pdf (planned for later) -Scheme similar to CFAR method to detect icebergs. -Show probabilities on a grey scale to discriminate between the different regions. B. Semi-automatic image classification using distribution matching -Matching the image to a known region type -Don’t throw the ice analysts knowledge away: use it ! -No prior knowledge of the distribution function -Using Kolmogorov - Smirov test (other can also be used)

Pearson diagram - Skewness vs. pmr Pearson diagram - kurtosis vs. pmr Classifying different region types using distributions

Background region Test region Compute the probability distribution function, P(I), for the test region Computation scheme for computing distributions:

Disko Bay, 9th April 2000.

PMR ‘image’.Gamma pdf ‘image’

Amplitude image from Cape Farewell - 11th March 2000

Gamma pdf ‘image’ PMR ‘image’

Amplitude image - Disko Bay 2/4/2000.

PMR ‘image’Gamma pdf ‘image’

Amplitude image - Disko Bay 24/5/2000.

PMR ‘image’Gamma pdf ‘image’

Distribution matching to the ice region shown in red -based on PMR ‘image’. Distribution matching to the water region shown in blue - based on Gamma pdf ‘image’.

Amplitude image from the East coast of Greenland, 22nd July 2000.

PMR ‘image’Gamma distribution ‘image’

KS matching to the blue water regionKS matching to the red ice region Dark values indicates maximum matching

Amplitude image 29th June Time when you most need help ! 1/10 ice, floes size 5-10 m

PMR ‘image’Gamma distribution ‘image’. Not so convincing

Conclusions. 1. Gamma distribution ‘images’ -Useful for determing the open water region in ice pack, near the coasts and in-land regions. -Easier to interpret than PMR -Complement the PMR ‘images’ Again like PMR, and all other texture parameters, Gamma ‘images’ are also ambiguous. All perform poorly when their help is most needed.

2. KS-Distribution matching: -Performs o.k. for the sea ice along the East and West coasts of Greenland. Shows potential for semi-automatically classifying an image. Advantage: Taps on the ice analysts experience of image interpretation. Currently 3 products operational: PMR, CFAR and now Gamma