the morphological filtering of the remote sensing images for the noise reduction comparing to...

THE MORPHOLOGICAL FILTERING OF THE REMOTE SENSING IMAGES FOR THE NOISE REDUCTION COMPARING TO TRADITIONAL FILTERS

M. Sc. Magdalena Jakubiak, Intergraph Poland

Ph. D. Przemysław Kupidura, Warsaw University of Technology

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The aim of the research:

The evaluation of the morphological filters in comparison to the non-morphological ones in their ability of noise removement at the remote sensing images.

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How the aim was reached:

Choice of the reference images, Artificial noises added to the images, Filtering ( non-morphological and

morphological filters), Comparison of the efects of the filtering, Evaluation, Conclusions.

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Choice of the reference images Images with different spatial resolution

and from differents systems: Landsat ETM+, Spot 5 ano aerial camera,

Images contain urban and rural areas.

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Artificial noises added to the images

Gaussian noise, „Salt and pepper” noise, All noises were generated and added in

ImageJ software.

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Image 1 – LANDSAT ETM+a

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Image 2 – SPOT 5a

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Image 3 – aerial photoa

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Filtering

Non-morphological filter Mean filter, Median filter, Frost filter, Kernel size: 3x3, 5x5, 7x7, 9x9, 11x11, Software: Idrisi32, Erdas.

Morphological filter Alternate filter, Alternate filter with Multiple Structuring Function, Element size: 3x3, 5x5, 7x7, 9x9, 11x11, Software: BlueNote.

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Comparison of the efects of the filtering

Indicators: correlation coefficient, Signal-to-Noise Ratio (SNR), Peak Signal-to-Noise Ratio (PSNR), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), correlation coefficient for edges.

Software: ImageJ

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Evaluation

Basic criteria: correlation coefficient and correlation

coefficient for edges for image after filtering higher than for noised image,

SNR and PSNR value for image after filtering higher than for noised image,

RMSE and MAE value for image after filtering lower than for noised image,

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Evaluation

Complementary criteria correlation coefficient and correlation

coefficient for edges for image after filtering with the highest calculated value,

SNR and PSNR value for image after filtering with the highest calculated value,

RMSE and MAE value for image after filtering with the lowest calculated value,

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Results – Gaussian noise σ=10

kernel/element

size

Frost filter

Alternate filter

Alternate filter with

Multiple Structuri

ng Function

Mean filter

Median filter

Median sequential filter

3x3 0,718 0,654 0,742 0,646 0,695 0,695

5x5 0,708 0,582 0,657 0,547 0,564 0,673

7x7 0,722 0,533 0,679 0,504 0,571 0,656

9x9 0,718 0,508 0,609 0,468 0,542 0,645

11x11 0,703 0,508 0,625 0,447 0,520 0,639

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Alternate filter with Multiple

Structuring Function with

element size 3x3,

Frost filter with kernel size 7x7,

Frost filter with kernel size 3x3 i

9x9.

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Image 2 – SPOT 5a

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kernel/element

size

Frost filter

Alternate filter


Multiple Structuri

ng Function

Mean filter

Median filter


3x3 0,354 0,505 0,468 0,581 0,537 0,537

5x5 0,364 0,488 0,542 0,524 0,485 0,556

7x7 0,382 0,470 0,541 0,488 0,514 0,557

9x9 0,404 0,451 0,528 0,456 0,497 0,557

11x11 0,429 0,437 0,524 0,441 0,485 0,557

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Mean filter with kernel size 3x3,

Median sequential filter with

triple, fourfold, fivefold kernel

size 3x3,


double kernel size 3x3.

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Image 2 – SPOT 5a

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Results – Gaussian noise

kernel/element

size

Frost filter

Alternate filter


Multiple Structuri

ng Function

Mean filter

Median filter


3x3 0,536 0,580 0,605 0,614 0,616 0,616

5x5 0,536 0,535 0,600 0,535 0,524 0,615

7x7 0,552 0,501 0,610 0,496 0,543 0,606

9x9 0,561 0,480 0,569 0,462 0,519 0,601

11x11 0,567 0,472 0,574 0,444 0,503 0,598

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Results – Gaussian noise

Median filter with kernel size 3x3,


double kernel size 3x3,

Mean filter with kernel size 3x3.

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Results – „salt and pepper” noise

kernel/element

size

Frost filter

Alternate filter


Multiple Structuri

ng Function

Mean filter

Median filter


3x3 0,487 0,966 0,972 0,861 0,976 0,976

5x5 0,496 0,921 0,968 0,862 0,937 0,972

7x7 0,502 0,845 0,971 0,875 0,942 0,967

9x9 0,515 0,425 0,952 0,873 0,927 0,964

11x11 0,525 0,225 0,952 0,865 0,908 0,961

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Results – „salt and pepper” noise

Median filter with kernel size

3x3,



element size 3x3,



element size 7x7.

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Image 2 – SPOT 5a

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Conclusions

When there is a choice of the methods of the

filtering, the kind of the noise should be taken

into consideration before making a decision.

The Alternate filter (opening-closeing

operations) gives satisfying result in

removing researched noises, but also causes

a significant edges degradation.

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Conclusions

Alternate filter with Multiple Structuring Function

has ability to preserve edges during noise

removing.


gives very good results for all kinds of noises.


appeares as the most universal filter.

THE MORPHOLOGICAL FILTERING OF THE REMOTE SENSING IMAGES FOR THE NOISE REDUCTION COMPARING TO TRADITIONAL FILTERS

M. Sc. Magdalena Jakubiak, Intergraph Poland

Ph. D. Przemysław Kupidura, Warsaw University of Technology

the morphological filtering of the remote sensing images for the noise reduction comparing to...

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