Automated Sea Ice Classification on dual- polarimetric...

Automated Sea Ice Classification on dual-polarimetric TerraSAR-X data Rudolf Ressel (Maritime Security Research, DLR)

> Lecture > Author • Document > Date DLR.de • Chart 1

incident of the research vessel Akademik Shokalskiy trapped in pack ice

30.12.2013 TSX ScanSAR image

Motivation: assist navigation through ice-infested waters

Chart 2 Rudolf Ressel• 28 Jan. 2015

Main steps of the algorithm:

1. Select features

Ice type classification Aim: Classify the ice in near real time

TerraSAR-X image (ScanSAR)

2. Feed the features into a neural network neural network

feature extraction feature a feature c feature b

fast ice First year thick ice First year ice open water with <1/10 ice of unspecified SoD open water unknown

Chart 3

20 km

• Wide footprint desirable for best navigational insight • Wide coverage in case of TerraSAR-X comes at the price of obtaining only

single polarization data

• Only classical image analysis possible, eg. texture features/second order statistics, image segmentation

• fully operatorless algorithms have not yet been achieved (season, geography, principal limitation of pure texture analysis)

explore polarimetric satellite SAR data

Ice Charting based on Single Polarization data


Dualpol data: Svalbard 2014/05/25, 14:33


Algorithmic approach

• Stripmap for widest dual-pol footprint available from TerraSAR-X

• Analysis based on lexicographic and Pauli basis covariance matrices

• To avoid computational overhead, investigate informational content of features by means of mutual information

• Supervised classification (neural network)


Features and classes input into classification

Dominant classes: first year ice, gray ice , gray-white ice, open water Eigendecomposition based features: H/A/α , α1 Non-Eigendecomposition based features: • Span : tr(T)

• Geometric intensity: μ = (det(T))1/2


• Copol Power Ratio <|SHH|²><|SVV|²>

• Phase difference: Δφ = φHH - φ VV

• Real part of copol cross product: |Re < SHH S∗

VV>|

• Correlation: <SHH S∗

VV><|SVV|²><|SHH|²>

• Surface scattering fraction: <|SHH +SVV|²>

span(T)

• Scattering Diversity: (1 - || T/span(T) ||²Frob)

Features and classes input into classification , continued


Discriminative Power of features of different features


RGB overlay of geometric intensity, scattering diversity, copol power ratio

H/α by ice type


… 3. Correlation, 4. Entropy H, … 8. Anisotropy, .., 10. Scattering Diversity H,A, and Scattering Diversity share a high amount of mutual information

Redundancy of features


Entropy H

Visual comparison H,A, Scattering Diversity


Anisotropy A Scattering Diversity

• By two-class discriminative power (listing only top two most relevant features per class pair): o Copol power ratio o geom. intensity o span o real part of copol cr.prod. o surface scattering fraction o Scattering Diversity

• By all-class discriminative power (decreasing relevance, omitting H,A) Geom. Intens., span, copol power r., real part copol cr.prod. , surface sc.fr., α, Scattering Diversity

Relevance of features


Conclusion: Eigendecomposition not needed, for reasons of avoiding computational overhead and memory consumption, use only:

• Copol power ratio • geometric intensity • scattering diversity, • real part of copol cross product • Span • surface scattering fraction • local variances of above features

Conclusion of relevance and redundancy analysis


• FANN library, 3 hidden layers, 6/12 input neurons, 4 output neurons (ice types)

• Result on validation dataset : 1. Copol power ratio, geometric intensity, scattering diversity; local variance

2. Copol power ratio, geometric intensity, scattering diversity, real part of copol cross product, span, surface scattering fraction; local variance

Classification: Neural network


Ice Type OW FY Ice GW Ice Gray Ice

Accuracy 90.0% 87.5% 76.0% 82.5%

Ice Type OW FY Ice GW Ice Gray Ice

Accuracy 92.7% 92.9% 83.5% 81.7%

Classification Result


Open Water

Ice classification

Gray Ice

First Year Ice Gray-white Ice

Features used: copol power ratio, geometric intensity, Scattering diversity, local variances of copol mentioned features

Classification Result


Open Water

Ice classification

Gray Ice

First Year Ice Gray-white Ice

Features used: copol power ratio, geometric intensity, Scattering diversity, span, real part of copol cross product, surface scattering fraction; local variances of mentioned features

• Sea ice classification works well on dualpol TSX data • Eigendecomposition based features (H/A) are redundant or irrelevant (α) in

terms of information content • Obtain more data over different geographical regions during different seasons

to explore generalizability • Optimize runtime for applicational purposes Literature: [1] Skrunes, S., Brekke, C., and Eltoft, T. (2014). Characterization of Marine Surface Slicks by Radarsat-2 Multipolarization Features. Geoscience and Remote Sensing, IEEE Transactions on, 52(9), 5302–5319 [2] Praks, J., Koeniguer, E., and Hallikainen, M. (2009). Alternatives to Target Entropy and Alpha Angle in SAR Polarimetry. Geoscience and Remote Sensing, IEEE Transactions on, 47(7), 2262–2274 [3] Marino, A. , Dierking, W. , Hajnsek, I. and Wesche, C. (2013): SAR polarimetry for classification of sea ice: a comparison of physical based algorithms on ICESAR data , ESA Living Planet Symposium, Edinburgh, Scotland, 9 September 2013 - 13 September 2013 [4] Dierking, W.; Skriver, H.; Gudmandsen, P. SAR polarimetry for sea ice classification. In: Applications of SAR Polarimetry and Polarimetric Interferometry. 2003. S. 18.

Conclusion and work in progress


Automated Sea Ice Classification on dual- polarimetric...

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