Maa-57.2040 Kaukokartoituksen yleiskurssi General Remote Sensing Image restoration

1. Maa-57.2040 Kaukokartoituksen yleiskurssiGeneral Remote SensingImage restoration Autumn 2007 Markus Törmä Markus.Torma@tkk.fi

2. Digital image processing • Image is manipulated using computer • Image  mathematical operation  new image • Application areas: • Image restoration • Image enhancement • Image interpretation / classification

3. Image restoration • Errors due to imaging process are removed • Geometric errors • position of image pixel is not correct one when compared to ground • Radiometric errors • measured radiation do not correspond radiation leaving ground • Aim is to form faultless image of scene

4. Image enhancement • Image is made better suitable for interpretation • Different objects will be seen better  manipulation of image contrast and colors • Different features (e.g. linear features) will be seen better  e.g. filtering methods • Multispectral images: combination of image channels to compress and enhance imformation • ratio images • image transformations • Necessary information is emphasized, unnecessary removed

5. Digital image processing • Analog signal: • phenomena is described of measured continuosly according to time or space • Digital signal: • analog signal is sampled with some interval • 2-dimensional digital signal digital image

6. Digital image processing Image function f(x,y): • Function according to spatial coordinates x and y • Value of function in position (x,y) corresponds to brightness of image in corresponding position

7. Digital image processing • Digital image consists of individual picture elements, pixels, which form discere lattice in spatial domain (x,y) • Digital image can also be presented using SIN-waves with different frequencies and amplitudes • called frequence domain (u,v) • Fourier transform is used to determine frequencies • Manipulation of digital image: • Image domain: pixel values are modified directly at iomage using some algorithm • Spatial domain: frequencies and amplitudes of SIN-waves are modified

8. Sources of error Movement opf imaging platform • Changes in height or speed • Attitude of satellite Image provider should correct Instrument • Scanning or measurement principle • Failures of sensors • Production methods or accuracy of instrument Calibration of instrument

9. Sources of error Atmosphere • Attenuation of radiation and decrease of contrast • Image is blurred Difficult to correct Object • Roundness of Earth • Rotation of Earth • Topography It is possible to cerrect these quite well

10. Geometric correction • Position of image pixel is not correct when compared to ground • Errors due to instrument, movement of imaging platform and object are removed • Known errors in geometry: • Earth cirvature and rotation • Topography • Imaging geometry • Rectification to map projection • Geometric transformation • Interpolation of pixel digital numbers

11. Geometric correction • Geometric correction is made • automatically using orbital parameters or • manually using ground control points • Alternatives • orbital parameters • ground control points • orthocorrection • Most accurate results by combining all

12. Raw image data • It can be difficult to recognize ground features from raw image data, because they are not necessarily similar than in nature

13. Raw image vs. corrected image

14. Geometric correction using orbital parameters • Information about • position of satellite (XYZ) • attitude () • Correction can be low quality due to poor orbital information • Knowledge about scanning geometry, movement of object and topography (DEM) will increase accuracy

15. Geometric correction • Accuracy of correction depends on quality of used information • Some examples about accuracy using orbital parameters: • NOAA AVHRR: 5 km - 1.5 km • Spot 1-4: 350 m • Spot 5: 50 m • Ground control points: accuracy should be better than 1 pixel • depends also mathematical model and topographic variations • Orthocorrection most accurate

16. Geometric correction Manual correction: • Ground control points (GCPs): • image coordinates are measured from image • map coordinates from map or georeferenced image

17. Geometric correction • GCPs are known and well distinguished ground features • crossroads, buildings, small lakes, small islands, features in waterline • More GCPs is better • When transformation between image coordinate system and map coordinate system is defined using polynomials, minimum number of points: • 1st degree polynomial: 3 points • 2nd degree: polynomial: 6 points

18. Example • Erdas Imagine • Old Landsat TM-image is georeferenced to same map coordinate system than Landsat ETM-image

19. Example • 2nd degree polynomial • 15 GCPs

20. Automatic correction • Software searches corresponding points from image to be georeferenced and image in map coordinate system • This can be based on • correlation between subimages • recognizable features (linear like roads or lakes) • These points are used as GCPs • Software produces many (e.g. 200-300 points for Landsat ETM-image) • user has to select which can be used • Automatization is needed when there are many images and/or it has to be made daily

21. Topographic error • Property of imaging system using central projection • Image of object is in incorrect place due to height variations

22. Orthocorrection • Topographic error is removed by changing the perspective of image from central projection to orthogonal projection • DEM is needed

23. Interpolation of digital numbers • Digital numbers for pixels of corrected image must be interpolated from uncorrected image • Seldomly number from uncorrected image can be used directly • Methods • nearest neighbor interpolation • bilinear interpolation • cubic convolution interpolation

24. Nearest neighbor interpolation • Take value of closest pixel from uncorrected image • Easy to compute • Values do not change • Result can be inaccurate korjattu kuva alkuperäinen kuva

25. Nearest neighbor interpolation • Values of some pixels are chosen more than once, some not at all • ”Piecewise” image • Linear features can disappear korjattu kuva alkuperäinen kuva

26. Bilinear interpolation • 4 closest pixels from uncorrected image are used • Average weighted by distance • Changes digital numbers • corresponds to average filtering korjattu kuva alkuperäinen kuva

27. Cubic convolution • 16 (4x4) closest pixels from uncorrected image are used • Smaller interpolation error than NN or BL-interpolation korjattu kuva alkuperäinen kuva

28. Alkuperäinen kuva lähin naapuri bilineaarinen kuutio

29. Image formation • Scene f(x) • Acquired image g(x) • Scene f(x) is corrupted by atmosphere and instrument in imaging process • They act like filters h(x)

30. Image formation • Image acquisition can be modelled with image degradation model: f(x) * h(x) + n(x) = g(x) g(x): acquired image h(x): filter corresponding to averaging effects due to atmosphere and instrument n(x): random errors due to instrument and data transmission f(x): scene

31. Inverse filtering • Errorness image of scene f(x) should be acquired by making inverse process to known image g(x) • Image degradation model in frequency domain: G(u)=F(u)H(u)+N(u) • Ideal inverse filtering Fe(u) = G(u)/H(u) - N(u)/H(u) • In practice difficult to solve • zeros in H(u) • effect of N(u) increases • Usually radiometric correction is divided to different phases which are corrected individually

32. Radiometric correction • In order that measurements taken with • different instruments • differents dates are comparable • Aim: radiance or reflectance • Radiance (W/m2/sr): • physical term which describes intensity of radiation leaving ground to some direction • Reflectance: • Radiance / incoming irradiance

33. Instrument calibration • Instruments are calibrated before satellite launch • measurements of known targets • instrument response is followed by measuring calibration targets in instrument or stable targets on ground • Each channel have calibration coefficients GAIN and OFFSET • these can vary within time • response decreases, so same target looks more dark

34. Instrument gain • Pixel digital number is multiplied with gain radiance = Dn * Gain • Gain = Lmax – Lmin / 255 • Lmax: largest radiance which can be measured by instrument • Lmin: smallest radiance which can be measured by instrument

35. Instrument offset • Background noide detected by instrument • Measurement, when instrument do not receive any radiation = Lmin

36. Radiometric correction • Equation: R = (Lmax-Lmin)/255*DN + Lmin OR R=Gain*DN + offset

37. Other corrections • Sun zenith angle: DN’=DN / SIN(sunq) • Ground is illuminated differently when Sun zenith angle changes • seasonal differences

38. Other corrections : • Distance between Sun and Earth • This distance varies according to seasons • Irradiance incoming to ground is when taking sun zenith angle into account:

39. Atmospheric correction • Absorption and scattering • Decrease of diffuse skylight • due to atmospheric scattering • largest at smaller waveleghts (blue), decreases as wavelenght increases • dampens image contrast

40. Atmospheric correction • REF: reflectance of pixel • Lsat: radiance measured by instrument • Lhaze: radiance due to atmospheric scattering (diffuse skylight) • TAUv: atmospheric transmittance from ground to instrument • E0: Sun spectral irradiance outside atmosphere, including effect of distance between Sun and Earth: E0 = E / d2, where E is Sun spectral irradiance outside atmosphere and d is distance between Sun and Earth in Astronomical Units • sz: Sun zenith angle • TAUz: atmospheric transmittance from Sun to ground • Edown: maanpinnalle tullut ilmakehän sironnan vaikutus

41. Atmospheric correction Apparent reflectance model • Removes effects of changing Sun-Earth distance and Sun zenith angle • changing imaging geometry • Does not remove atmospheric effects: absorption or scattering • Following parameters for correction model: TAUz: 1.0, TAUv: 1.0, Edown: 0.0, Lhaze: 0.0

42. Atmospheric correction DARK-OBJECT-SUBTRACTION • It is supposed that there are areas in image that are on shadow, so that all radiation coming to instrument from these areas is due to diffuse skylight • Following parameters for correction model : TAUz: 1.0 TAUv: 1.0 Edown: 0.0 Lhaze: measure radiance from target which is in shadow (like shadow of cloud) or does not reflect radiation (water in infrared)

43. Atmospheric correction CHAVEZ • Modified DOS • Atmospheric transmittances are approximated by angles of imaging geometry • Following parameters for correction model : TAUz: cos(sz) TAUv: 1.0 or cos(incidence angle) Edown: 0.0 Lhaze: measure radiance from target which is in shadow (like shadow of cloud) or does not reflect radiation (water in infrared)

44. Atmospheric correction • More theoretic methods try to model how radiation travels in atmosphere • Aim is to model • atmospheric transmittance and absorption • scattering due to gases • reflectances due to atmosphere, not ground • Difficult, requires lot of computing and precise knowledge about the state of atmosphere

45. VTT atmospheric correction • SMAC: Simplified Method of Atmospheric Correction • Removes Rayleigh scattering, absorption due to atmospheric gases, imaging geometry (sun anlges and distance) • Digital number reflectance • Needed calibration coefficients of instrument sun zenith and azimuth angles amount of water vapour ozone atmospheric optical depth

46. VTT atmospheric correction • Original Landsat ETM-images • Atmospheric optical Depth can be estimated from image if there are suitable channels • ETM7/ETM3 ratio should be about 0.4 for olf coniferous forests

47. VTT atmospheric correction • Corrected Landsat ETM-mosaic • Eastern Finland • 7 images • RGB: 321

48. VTT atmospheric correction Landsat ETM-mosaic of Northern Finland consists of 9 images

49. Dehazing image • Based on Tasselled Cap-transformation • TC4 image sensiive to atmospheric effects • Landsat-5 TM-image: TC4 = 0.8461*TM1 - 0.7031*TM2 - 0.4640*TM3 - 0.0032*TM4 - 0.0492*TM5 - 0.0119*TM7 + 0.7879 • Image channel is corrected by subtracting TC4 from it TMcx = TMx - (TC4 - TC40)*Ax TMcx: Corrected digital number of channel x TMx: Original digital number of channel x TC40: Value of haze-free pixel in TC4 Ax: Correction factor determined from image

50. Dehazing image • Original and corrected TM1