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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 aim of the research :.

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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 aim of the research
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.
how the aim was reached
Howtheaim was reached:
  • Choice of thereferenceimages,
  • Artificialnoisesadded to theimages,
  • Filtering ( non-morphological and morphologicalfilters),
  • Comparison of theefects of thefiltering,
  • Evaluation,
  • Conclusions.
choice of the reference images
Choice of thereferenceimages
  • Imageswithdifferentspatial resolution and fromdifferents systems: Landsat ETM+, Spot 5 ano aerial camera,
  • Imagescontainurban and ruralareas.
artificial noises added to the images
Artificialnoisesadded to theimages
  • Gaussiannoise,
  • „Salt and pepper” noise,
  • All noisesweregenerated and addedinImageJ software.
filtering
Filtering
  • Non-morphological filter
    • Meanfilter,
    • Median filter,
    • Frost filter,
    • Kernelsize: 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.
comparison of the efects of the filtering
Comparison of theefects of thefiltering
  • 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
evaluation
Evaluation
  • Basic criteria:
    • correlation coefficient and correlation coefficient for edges for imageafterfilteringhigherthan for noisedimage,
    • SNR and PSNR value for imageafterfilteringhigherthan for noisedimage,
    • RMSE and MAE value for imageafterfilteringlowerthan for noisedimage,
evaluation1
Evaluation
  • Complementarycriteria
    • correlation coefficient and correlation coefficient for edges for imageafterfilteringwiththehighestcalculatedvalue,
    • SNR and PSNR value for imageafterfilteringwiththehighestcalculatedvalue,
    • RMSE and MAE value for imageafterfilteringwiththelowestcalculatedvalue,
results gaussian noise 101
Results – Gaussiannoiseσ=10
  • Alternate filter with Multiple Structuring Function with element size 3x3,
  • Frost filter withkernelsize 7x7,
  • Frost filter withkernelsize 3x3 i 9x9.
results gaussian noise 251
Results – Gaussiannoiseσ=25
  • Mean filter withkernelsize 3x3,
  • Median sequential filter withtriple, fourfold, fivefoldkernelsize 3x3,
  • Median sequential filter with double kernelsize 3x3.
results gaussian noise1
Results – Gaussiannoise
  • Median filter withkernelsize 3x3,
  • Median sequential filter with double kernelsize 3x3,
  • Mean filter withkernelsize 3x3.
results salt and pepper noise1
Results – „salt and pepper” noise
  • Median filter withkernelsize 3x3,
  • Alternate filter with Multiple Structuring Function with element size 3x3,
  • Alternate filter with Multiple Structuring Function with element size 7x7.
conclusions
Conclusions
  • Whenthereis a choice of themethods of thefiltering, thekind of thenoiseshould be takenintoconsiderationbeforemaking a decision.
  • TheAlternate filter(opening-closeing operations) givessatisfyingresultinremovingresearched noises, but alsocauses a significantedgesdegradation.
conclusions1
Conclusions
  • Alternate filter with Multiple Structuring Function hasability to preserveedgesduringnoiseremoving.
  • Alternate filter with Multiple Structuring Function givesverygoodresults for allkinds of noises.
  • Alternate filter with Multiple Structuring Function appeares as the most universal filter.
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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