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Spatial and Spectral Evaluation of Image Fusion Methods

Spatial and Spectral Evaluation of Image Fusion Methods. Sascha Klonus Manfred Ehlers Institute for Geoinformatics and Remote Sensing University of Osnabrück. Content. Introduction Image Fusion Test Site Fusion Results Color Distortions Evaluation Methods and Results Ehlers Fusion

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Spatial and Spectral Evaluation of Image Fusion Methods

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  1. Spatial and Spectral Evaluation of Image Fusion Methods Sascha Klonus Manfred Ehlers Institute for Geoinformatics and Remote Sensing University of Osnabrück

  2. Content • Introduction Image Fusion • Test Site • Fusion Results • Color Distortions • Evaluation Methods and Results • Ehlers Fusion • Conclusions and Future Work

  3. Data Fusion: Why is it Necessary? • Remote sensors have different spatial resolution for panchromatic and multispectral imagery • The ratios vary between 1:2 and 1:5 • For multisensor fusion the ratios can exceed 1:30(e.g. Ikonos/Landsat)

  4. Objectives of Image Fusion • Sharpen images • Improve geometric corrections • Provide stereo-viewing capabilities • Enhance certain features • Complement data sets • Detect changes • Substitute missing information • Replace defective data Pohl & van Genderen (1998)

  5. Meaning of Pan-Sharpening Spatial + Spectral panchromatic & high geometric resolution multi-/hyperspectral image & low geometric resolution multi-/hyperspectral & high geometric resolution

  6. Fusion Methods • Color Transformations • Modified IHS Transformation • Statistical Methods • Principal Component Merge • Numerical Methods • Brovey • CN Spectral Sharpening • Gram-Schmidt Spectral Sharpening • Wavelet based Fusion • Combined Methods • Ehlers Fusion

  7. Test Site

  8. Original Data Quickbird Panchromatic image (2004-09-04) Quickbird Multispectral image (2004-09-04) Formosat Multispectral image (2004-01-30) Ikonos Multispectral image (2005-08-03)

  9. Single Sensor Fusion: Quickbird Fused with CN Spectral Sharpening Fused with Ehlers Fused with Brovey Fused with Gram-Schmidt Quickbird Multispectral image Fused with Wavelet Fused with modified IHS Fused with PC

  10. Multisensor Fusion: Ikonos Fused with Ehlers Fused with CN Spectral Sharpening Fused with Brovey Fused with Gram-Schmidt Fused with PC Fused with Wavelet Fused with modified IHS Ikonos Multispectral image

  11. Multisensor Fusion: Formosat Fused with Ehlers Fused with PC Fused with modified IHS Fused with Gram-Schmidt Fused with Brovey Fused with CN Spectral Sharpening Fused with Wavelet Formosat Multispectral image

  12. Fusion Problem: Color Distortion • Panchromatic band has a different spectral sensitivity • Multisensoral differences (e.g. Ikonos and SPOT merge) • Multitemporal (seasonal) changes between pan and ms image data • Inconsistent panchromatic information is fused into the multispectral bands

  13. Spectral Comparison Methods (1) • RMSE s = standard deviation org = Original image fused = Fused image x = Mean • Correlation coefficients • Visual (Structure and Colour Preservation)

  14. Results RMSE

  15. Results Correlation Coefficients

  16. Spectral Comparison Methods (2) Per Pixel Deviation Fused image (Formosat 2m) Degraded to ground resolution of original image(Formosat = 8m) Result: Vector containing the deviation per pixel Degrade Original multispectral image (Formosat 8m)

  17. Mean Per Pixel Deviation

  18. Spatial Comparison Methods (1) - Edge Detection - -

  19. Results Edge Detection

  20. Spatial Comparison Methods (2) Highpass Filtering Correlation

  21. Highpass Correlation Results

  22. FFT Filter Based Data Fusion (Ehlers Fusion) FFT FFT LPF HPF FourierSpectrum PanHP FFT-1 FourierSpectrum ILP ILP+PanHP H S R‘ G‘ B‘ I H S R G B IHS-1 Basis: IHS Transform and Filtering in the Fourier Domain Multispectral Image Panchromatic Image

  23. Panchromatic image and its spectrum Original panchromatic image Panchromatic Spectrum

  24. Filtersetting effects Intensity Frequency fn Cut-off Frequency Filtered Panchromatic Spectrum

  25. Effects in the spatial domain Filtered panchromatic image Fused image

  26. Filtersetting effects Intensity Frequency fn Cut-off Frequency Filtered Panchromatic Spectrum

  27. Effects in the spatial domain Filtered panchromatic image Fused image

  28. Filtersetting effects Intensity Frequency fn Cut-off Frequency Filtered Panchromatic Spectrum

  29. Effects in the spatial domain Filtered panchromatic image Fused image

  30. Results • Ehlers Fusion shows the best overall results in all images • It works also if the panchromatic Information does not match the spectral sensitivity of the merged bands (multitemporal and multisensoral fusion) • Its performance is superior to standard fusion techniques (IHS, Brovey Transform, PC Merge) • Wavelet preserves the spectral characteristics at the cost of spatial improvement • Ehlers Fusion is integrated in a commercial image processing system (Erdas Imagine 9.1)

  31. Future Work • Fusion of radar- and optical Data • Development of one method to evaluate the spatial and spectral quality of an fused image • Comparison with the algorithm of Zhang (PCI Geomatica) • Research on automation for filter design

  32. Thanks for your Attention Questions???

  33. Ehlers Fusion Program

  34. Ehlers Fusion Program

  35. Ehlers Fusion Program

  36. Ehlers Fusion Program

  37. Ehlers Fusion Program

  38. Multispectral image and its spectrum Original multispectral intensity Multispectral intensity spectrum

  39. Filtersetting effects Intensity Frequency fn Cut-off Frequency Filtered multispectral spectrum

  40. Filtersetting effects Intensity Frequency fn Cut-off Frequency Filtered multispectral spectrum

  41. Filtersetting effects Intensity Frequency fn Cut-off Frequency Filtered multispectral spectrum

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