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 evaluation of the morphological filters in comparison to the non- morphological ones in their ability of noise removement at the remote sensing images. 2

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. 3

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. 4

Artificial noises added to the images  Gaussian noise,  „Salt and pepper” noise,  All noises were generated and added in ImageJ software. 5

Image 1 – LANDSAT ETM+ a dc b 6

Image 2 – SPOT 5 a dc b 7

Image 3 – aerial photo a dc b 8

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. 9

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 10

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, 11

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, 12

Results – Gaussian noise σ=10 kernel/element size Frost filter A lternate f ilter A lternate f ilter with Multiple Structuring Functio n Mean filter Median filter Median sequential filter 3x3 0,718 0,654 0,742 0,6460,695 5x5 0,7080,5820,6570,5470,5640,673 7x7 0,722 0,5330,6790,5040,5710,656 9x9 0,718 0,5080,6090,4680,5420,645 11x110,7030,5080,6250,4470,5200,639 13

Results – Gaussian noise σ=10 14  Alternate filter with Multiple Structuring Function with element size 3x3,  Frost filter with kernel size 7x7,  Frost filter with kernel size 3x3 i 9x9.

Image 1 – LANDSAT ETM+ a c b 15

Image 2 – SPOT 5 a c b 16

Image 3 – aerial photo a c b 17

Results – Gaussian noise σ=25 kernel/element size Frost filter A lternate f ilter A lternate f ilter with Multiple Structuring Functio n Mean filter Median filter Median sequential filter 3x3 0,3540,5050,468 0,581 0,537 5x5 0,3640,4880,5420,5240,485 0,556 7x7 0,3820,4700,5410,4880,514 0,557 9x9 0,4040,4510,5280,4560,497 0,557 11x110,4290,4370,5240,4410,485 0,557 18

Results – Gaussian noise σ=25 19  Mean filter with kernel size 3x3,  Median sequential filter with triple, fourfold, fivefold kernel size 3x3,  Median sequential filter with double kernel size 3x3.

Image 1 – LANDSAT ETM+ a c b 20

Image 2 – SPOT 5 a c b 21

Image 3 – aerial photo a c b 22

Results – Gaussian noise kernel/element size Frost filter A lternate f ilter A lternate f ilter with Multiple Structuring Functio n Mean filter Median filter Median sequential filter 3x3 0,5360,5800,605 0,6140,616 5x5 0,5360,5350,6000,5350,524 0,615 7x7 0,5520,5010,6100,4960,5430,606 9x9 0,5610,4800,5690,4620,5190,601 11x110,5670,4720,5740,4440,5030,598 23

Results – Gaussian noise 24  Median filter with kernel size 3x3,  Median sequential filter with double kernel size 3x3,  Mean filter with kernel size 3x3.

Results – „salt and pepper” noise kernel/element size Frost filter A lternate f ilter A lternate f ilter with Multiple Structuring Functio n Mean filter Median filter Median sequential filter 3x3 0,4870,966 0,972 0,861 0,976 5x5 0,4960,9210,9680,8620,9370,972 7x7 0,5020,845 0,971 0,8750,9420,967 9x9 0,5150,4250,9520,8730,9270,964 11x110,5250,2250,9520,8650,9080,961 25

Results – „salt and pepper” noise 26  Median filter with kernel size 3x3,  Alternate filter with Multiple Structuring Function with element size 3x3,  Alternate filter with Multiple Structuring Function with element size 7x7.

Image 1 – LANDSAT ETM+ a c b 27

Image 2 – SPOT 5 a c b 28

Image 3 – aerial photo a c b 29

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. 30

Conclusions  Alternate filter with Multiple Structuring Function has ability to preserve edges during noise removing.  Alternate filter with Multiple Structuring Function gives very good results for all kinds of noises.  Alternate filter with Multiple Structuring Function appeares as the most universal filter. 31

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