Computer-based identification and tracking of Antarctic icebergs in SAR images Department of Geography, University of Sheffield, 2004 Computer-based identification and tracking of Antarctic icebergs in SAR images Tiago A.M. Silva, Grant R. Bigg Department of Geography, University of Sheffield, 2004 A Review By Aliah Chowdhury February 2006

Introduction Purpose Purpose  Antarctic Icebergs influence climate and ocean changes Application Application  Tracking iceberg activity by some efficient mechanism Methodology Methodology  Use previous image processing techniques on SAR images to identify icebergs  Extend method to include tracking of temporal changes

Outline  Previous Approaches  The Extension in this Study  The Algorithm  Implementation & Results  Discussions by the Author  Evaluation  Final Words & Questions

Previous Approaches to Iceberg Observation Manual Ship-borne observations Ship-borne observations – Limited Information – No ability to track – Unable to find all icebergs Satellite Imagery Satellite Imagery – Difficult to identify – Inefficient visual tracking – Unable to measure changes Computerised Initial use of IP Initial use of IP – Under sampling – Removes smaller objects – Merges close objects Improved IP Technique Improved IP Technique – Edge detection – Watershed Segmentation – Separates objects – Extracts shapes All of these have their own merit and serve different applications

This Study: Extend Image Processing Approach to Track Iceberg Activity

The Extended Feature Requirements Requirements – Detect and identify smaller icebergs – Separate from coastal ice sheets – Isolate icebergs from other objects – Track temporal changes & movement Most Suitable Method Most Suitable Method – Two Stage Classification – Simple ‘Parametric’ Classifier – Unsupervised and Quantitative

The Algorithm (1) Pre-processing Pre-processing – Block average with 2x2 window, under sample by 2 – Increases pixel width 12.5m to 25m: within ~26m res. Coastal Masking Coastal Masking – Reduce res. by 4, block average with 4x4, under sample – Operator marks polygons within each coastal region – Watershed segmentation; interpolation; binary masking Segmentation Segmentation – Multiresolution Ratio of Averages Filter; varied window sizes – Ratios in 4 direction windows normalised to build edge map – Watershed segmentation with threshold of -10dB – Oversegmentation solved by merging rule with minimum: 15% shared contour, |2dB| intensity difference

The Algorithm (2) Classification – Iceberg Classifying Classification – Iceberg Classifying The identification of icebergs is achieved using a simple parametric classifier with the following limits: Classification – Iceberg Tracking Classification – Iceberg Tracking Matching icebergs in images at different times/locations. 1.Paired & ranked by size similarities, then shape resemblance. 2.Size matching by minimum distance classifier between feature vectors composed of √area and major/minor axes. 3.Objects with <500m distance tested for shape matching 4.1D distance/direction vector resampled every 5° into shape vector 5.All similar objects compared by shifting vector, then classified

Iceberg Map & Database ESA PRI/IMP ImageImage Extraction and Calibration Edge Mapping Mask Coast?Polygon Markers Watershed Segmentation Hierarchical Watershed Select & Apply Threshold Watershed Segmentation 1 st Selection & Merging Iceberg Classification Review Classification Area Estimate Correction In/Out User Intervention KEY Coastal Masking Segmentation Offline Processing Online Processing YesNo

Conditions & Results Implementation Implementation – MATLAB Image Processing Toolbox, images in FFT – ESA BEST Software for object analysis Conditions 3 wintertime intensity images 3 wintertime intensity images Local methods of segmentation Local methods of segmentation 3x3 & 5x5 window sizes 3x3 & 5x5 window sizes Only σ° > -8dB classified icebergs Only σ° > -8dB classified icebergs Manual classification of icebergs Manual classification of icebergs – Brighter than background – Bright rim closer to sensor – Shadow away from sensor – Angular corners Manual tracking v computerised Manual tracking v computerisedResults Over 90% of iceberg correctly segmented over all images Over 90% of iceberg correctly segmented over all images ~70% of pixels classified correctly ~70% of pixels classified correctly Between % of all matches detected accurately Between % of all matches detected accurately Misses or mismatches due to incorrect segmentation Misses or mismatches due to incorrect segmentation Better detecting smaller icebergs Better detecting smaller icebergs But misses usually smaller icebergs But misses usually smaller icebergs 10-13% area underestimated 10-13% area underestimated

Simulation

Discussion by Authors σ° values for background, sea ice and icebergs overlap in images; σ° values for background, sea ice and icebergs overlap in images; Reduced values for icebergs can be due to recent phenomena; Reduced values for icebergs can be due to recent phenomena; Higher values for background due to sea ice concentration. Higher values for background due to sea ice concentration. Looking angle & pass direction differ between acquisitions. Looking angle & pass direction differ between acquisitions. Segmentation process affected by slopes of icebergs at edges. Segmentation process affected by slopes of icebergs at edges. Robust rotation feature untested Robust rotation feature untested Applicable to many areas of study Applicable to many areas of study Extendable to other Imagery Extendable to other Imagery

Evaluation of Study Use of previous methods constructive and good for validation Use of previous methods constructive and good for validation Classification stage most successful; failures mostly due to unavoidable circumstances Classification stage most successful; failures mostly due to unavoidable circumstances Utilises features and IP techniques to great effect Utilises features and IP techniques to great effect Blend of qualitative and quantitative measurements Blend of qualitative and quantitative measurements The study admits inconsistency and needs to address more rigorous validation methods The study admits inconsistency and needs to address more rigorous validation methods Useful potential, widely extendable, though needs refinement Useful potential, widely extendable, though needs refinement

Final Words & Questions THANK YOU!