Motion tracking of sea ice with SAR satellite data

Denis Demchev, Researcher at AARI, NIERSC

Outline

Description of sea ice feature tracking algorithm

Comparison with manual drift vectors, buoys and DTU drift product from S1a

Examples with S1a and RS2 for «gridded» version of the algorithm

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An algorithm is proposed for sea ice drift retrieval from sequential satellite synthetic aperture radar (SAR) imageries.

State-of-the-art image processing methods from Computer Vision (CV) are becoming actual for automatic SAR remote sensing data analysis.

Keypoints (KP) is used for feature tracking in CV. KP is a point which could be tracked from image to image.

The algorithm has to be robust against speckle noise, gray level variance and ice floe rotation. This could be solved by keypoints detection using Scale Invariant Feature Transform (SIFT). The modified version of SIFT is adopted for satellite SAR data including Sentinel-1a.

Keypoints

Descriptors

Detect keypoints

Describe each region

around a keypoint as a

feature vector

(descriptor)

Compare descriptors

by descriptor distance or similarity

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Basic idea

Method - create a Gaussian pyramid - Keypoint localization (local extrema in the fields of the pyramids) (a) frame around an interest point, oriented according to the dominant gradient direction; (b) an 8 bin histogram over the direction of the gradient in a part of the grid; (c) histograms are extracted in each grid location; (d) the histograms are concatenated to form one long feature vector.

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Method

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Keypoints detection

Keypoints description

Matching

Modifications

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The benefit of Gaussian blurring is the suppression of noise, but relevant image structures are blurred and drift away from their locations

A good solution to make the blurring locally adaptive, yielding the blurring of noise, while retaining details

Replace Gaussian scale space with non-linear scale space using anisotropic diffusion proposed by Perona and Malik [1990]

Method

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1. The classic nonlinear diffusion equation is: where div and 𝛻𝛻 are respectively the divergence and gradient operators, and 𝐿𝐿 is the image luminance;

2. A conductivity function 𝑐𝑐 make the diffusion adaptive to the local image structure.

Time 𝑡𝑡 is the scale parameter, and larger values lead to simpler image representations. In anisotropic diffusion the image gradient magnitude controls the diffusion at each scale level. This way, the conductivity function 𝑐𝑐 is defined as:

𝛻𝛻𝐿𝐿𝜎𝜎 is the gradient of a Gaussian smoothed version of the original image L;

3. We use one of the two conductivity function proposed by Perona and Malik:

The parameter λ is the contrast factor that controls the level of diffusion. It determines which edges have to be enhanced or kept and which ones have to be cancelled.

Gaussian scale-space versus non-linear diffusion scheme

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Gaussian kernel

Non-linear diffusion scheme

Blurring

Experiments

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NIFT Validation

Buoy # 784540 Example for: 01 March, 13GMT – 03 March, 15GMT, 2016

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Experiment #1

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Ice drift for 2016-03-31 / 2016-04-02 from HH Sentinel-1a

Experiment #1

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PROPOSED SIFT ORB

Sea Ice Drift field

Start points distribution

Experiment #1

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PROPOSED SIFT ORB

Start points

Data density

Experiment #1

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Experiment #2

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Ice drift for 2016-03-02 / 2016-03-03 from HV Sentinel-1a

Experiment #2

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PROPOSED SIFT ORB

Sea Ice Drift field

Start points distribution

Experiment #2

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PROPOSED SIFT ORB

Start points

Data density

Experiment #2

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Comparison with MCC (Exp. #3)

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Ice drift for 2016-05-02 / 2016-05-03 from HV Sentinel-1a

Visualization of vectors, retrieved with proposed (a) and MCC (b) algorithms for experiment 3. At (a) and (b) highlighted box, scaled at (c): MCC vectors given with black arrows, AKAZE vectors given with white arrows.

a b c

Comparison with MCC (Exp. #4)

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Ice drift for 2016-05-02 / 2016-05-03 from HV Sentinel-1a

Visualization of vectors, retrieved with proposed (a) and MCC (b) algorithms for experiment 4. At (a) and (b) highlighted box, scaled at (c): MCC vectors given with black arrows, AKAZE vectors given with white arrows.

a b c

Comparison with MCC

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Comparison with DTU drift product

RMS difference to reference data MCC: ~ 421 m/day

PROPOSED: ~238 m/day

Higher spatial resolution of ice drift product

Proposed algorithm could catch ice motion features at scale of less than 1 km using initial data with spatial

resolution of 100 m

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Gridded Product

a new «gridded» version of the algorithm is developed

based on optimization of feature point detection within image blocks

gridded product of 1 km resolution from 100m initial data

tested with Sentinel-1a/Radarsat-2

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Radarsat2 example

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Sentinel-1a example

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Publications and reports

First time reported at IICWG’2013: ftp://sidads.colorado.edu/pub/projects/noaa/iicwg/IICWG-2013/Demchev_Ice_Drift_Retrieval_Algorithm_From_SAR.pdf Sea ice drift tracking from SAR using Accelerated-KAZE

features. Demchev et all. Near submitting in Journal of Applied Remote Sensing (JARS)

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Thank you for your attention!

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