1 Challenge the future Coastal Image Classification Bas Hoonhout, Max Radermacher, Fedor Baart.

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1Challenge the future

Coastal Image ClassificationBas Hoonhout, Max Radermacher, Fedor Baart

Coastal Image Classification

Why is it useful?

How does it work?

1. Segmentation (superpixels)Create clusters of pixels with similar intrinsic properties

2. Feature extractionExtract as much information as possible from superpixels

3. Model construction and trainingTrain a model to discriminate between classes using features

4. Model predictionPredict classification of an unseen image

Step 1: segmentation (superpixels)

Intensity: R, G, B, C, Y, M, K, …Position: N, M… and gradients and filters

Intensity, position and gradientsShape, texture, …Variance, frequency, …

Step 2: feature extraction

Step 2: feature extraction16 channels and1727 featuresper superpixel

Step 3: model construction and training

f1 … fK

Ψ01 Ψ0M

… …

ΨN1 ΨNM

Ψ10 … Ψ1M

ΨN0 ... ΨNM

pixel intensity

sea beach

et cetera, until we have a 1727-dimensional feature space

Step 4: model prediction

Performance

• Ongoing research!

• 192 manually annotated Argus imagesdoi:10.4121/uuid:08400507-4731-4cb2-a7ec-9ed2937db119

• Training set 75%, test set 25%

• Last benchmark test: >90% correct

• Target benchmark test: >95% correct

Take home messages

Classify coastal images?Spend your creativity on features!

Classify large datasets?Go for full automation!

What would you do with 95%accurate, automated classificationof coastal images?

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