0602_izak Marais Presentation

On-board Image Quality Assessment for a Satellite

I.v.Z. Marais Prof W.H. Steyn Prof J. du Preez

Stellenbosch UniversityDepartment of Electrical and Electronic Engineering

7th IAA Symposium on Small Satellites for Earth Observation,5 May 2009

Marais, Steyn, du Preez (SU) On-board Image Quality Assessment IAA Symposium, May 09 1 / 31

Outline

1 Motivation

2 Quality Features UsedCloud CoverSensor Noise LevelTelescope Defocus Extent

3 Quality Assessment ModelCreating the ModelTesting the integrated system


Motivation

Outline

1 Motivation




Motivation

Satellite Design ConstraintsStorage Capacity Exceeds Download Capacity

Figure: LEO satellite footprint.

Limited download capacityShort downlink time(low earth orbit).Limited bandwidth.

Big storage capacityStorage cheaper than bandwidth.


Motivation

How On-Board Processing Can Help

ProblemMore images can be acquired and stored than downloaded.

SolutionUse on-board processing to ensure best images are downloaded first.


Motivation

How Image Processing Is Used

Estimate 3 image quality features.Combine features into quality measurebased on quality model.Sort images according to quality measure.Download images from top of sorted quality list.


Quality Features Used

Outline

1 Motivation




Quality Features Used Cloud Cover

Cloud Cover as a Quality Feature

Resource satellites map earth’s surface.Cloud cover = interference.

Figure: Differing levels of cloud cover.



Cloud Detection Algorithms

Two categories.

Spectral DomainSpectral signature must be bright (visible band) and cold (thermalband) enough, i.e., greater than thresholds.Prevalent technique since 1965.

Spatial DomainTexture features are used in a pattern recognition system.Requires more training data and are processor intensive.Less commonly used.



Region Growing AlgorithmBackground

Used on board Surrey satellite for cloud detection.Algorithm combines ideas fromspectral and spatial domains.

Figure: Cloud cover extracted by region growing algorithm.



Region Growing AlgorithmSummary

Start at bright centre pixel.Grow region up to a fixed size by adding brightest pixels from theboundary to the region.Segment when contrast across the boundary and between thearea and the boundary is the greatest.



Comparative Results for Cloud DetectionRegion Growing vs Thresholding

Thresholding has performance advantage.Region growing computationally more expensive.

0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16Segmentation error

Test

Train

Region GrowingThreshold

Figure: Comparison of segmentation errors on Landsat 5 data.



Using Multiple Channels for Cloud DetectionBackground

ProblemMultiple image channels available.Cannot fit all channel into satellite memory.Which is best for cloud detection?

SolutionUse a single channel or weightedcombination.Evaluate different combination techniquesfrom cloud detection literature.Novel application of speech processingmethod: HDA.



Comparison Between Multiple ChannelsResults

HDA gave best results, both quantitatively and qualitatively:

0.00 0.02 0.04 0.06 0.08 0.10 0.12Segmentation error

D

HOT

HDA

Blue

Red

TrainTest

Figure: Quantitative comparison Figure: Qualitative comparison


Quality Features Used Sensor Noise Level

Noise as a Quality Feature

Noise from different sources can corrupt images.Global additive Gaussian encountered in literature. Relativelydifficult to remove.

Figure: Different types of noise in satellite images.



Noise Estimation Algorithms

Blind, automatic estimation.Two algorithms were compared.Both divide the image into small blocks and determine thevariance of each block.Noise variance calculated differently:

1 Fixed block size. Uses variance histogram peak.2 Varying block size. Separates image variance from noise variance

based on typical noise variance order statistics.



Comparative Results for Noise EstimationFixed Block Size vs. Varying Block Size

Varying block size method superior.Better accuracy in low noise conditions.Determines signal noise separation.Does not estimate when separation is poor.Adapted to be more conservative: performance increased forremote sensing images.


Quality Features Used Telescope Defocus Extent

Blur as a Quality Feature

Temperature variations can cause lens to become defocused.

Figure: Input image and blurred image.



Blur Estimation AlgorithmsGeneral

Class of problems: Blind Image Deconvolution.

Figure: g(x , y) = f (x , y) ∗ h(x , y) + n(x , y)

Difficult problem – access to g(x , y) only,want to determine h(x , y).Requires assumptions about imaging system.



Class of Blur Estimation Algorithms Evaluated

Typical type of blur function assumed (defocus blur).Use frequency domain to identify blur function parameters.Spatial domain g(x , y).Frequency domain G(u, v) = F{g(x , y)}.Cepstral domain Cg(p, q) = F−1{log G(u, v)}.



Detecting Failure in Very Noisy ConditionsIs the blur estimate reliable?

In absence of noise methods give clear blur-related peak.

cepstrum side viewblurry image cepstrum

No noise: (Er large)

Given enough noise blur detection will fail.

Noise: (Er small)

Novel relative energy measure detects failure. Er =EpeakErest



Four Blur Estimation Algorithms Compared

Cepstral algorithm sensitive to noise.Variations investigated to increase robustness.

ExistingBicepstrumSpectral subtraction pre- andpost-processing.

NovelSpectral subtraction combined withpower spectrum angular smoothing.

Figure: Power spectrumangular smoothing.



Comparative Results for Blur Estimation

Performance acceptable innoiseless conditions.Cepstral methodvery sensitive.Novel angular smoothing hasbest performance.



Embedded Implementation of Algorithms

Promising algorithms implemented on embedded architecture.SH4 architecture similar to Sumbandilasat.Performance feasible.


Quality Assessment Model

Outline

1 Motivation




Quality Assessment Model Creating the Model

Subjective Quality Assessment Experiment

Figure: Experiment user interface.

Combine quality features intoa single score.Base model on subjectiveevaluation.Online experiment designed.More than 18000 independentjudgements recorded.


Quality Assessment Model Creating the Model

Quality Assessment Model

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Cloud cover[%]Blur radius

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Two models evaluated:1 Mixed spline model - manually tuned

to have more detail in relevant areas.2 Neural net model.

Spline model gives better predictionresults.


Quality Assessment Model Testing the integrated system

Total System Performance

Feature estimation combinedwith quality model.Predicted quality correlateswell with human perception.Linear correlation coefficient of88%.


Summary

Summary

Novel blur estimation algorithm developed and application of HDAto cloud detection – favourable performance.Image quality assessment model developed based on largeexperiment.System quality predictions agree with human quality predictions.


Appendix For Further Reading

For Further Reading

I.v.Z. Marais and W.H. SteynRobust defocus blur identification in the context of blind imagequality assessment.Signal Processing: Image Communication, November 2007.

I.v.Z. Marais, W.H. Steyn and J.A. du PreezConstruction of an image quality assessment model for use onboard an LEO satellite.IEEE Geoscience and Remote Sensing Symposium, Boston, MA,July 2008.

I.v.Z. Marais, J.A. du Preez and W.H. SteynAn optimal image transform for threshold-based cloud detectionusing heteroscedastic discriminant analysis.International Journal of Remote Sensing, accepted.


Appendix For Further Reading

The End

Thank you for listening.Any Questions?


0602_izak Marais Presentation

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