 | Correction Process for Non-systematic Distortions and ... |
Correction Process for Non-systematic Distortions
Locating Ground Control Points This process employs identification of geographic features on the image called ground control points (GCPs), whose position are known such as intersection of streams, highways, airport, runways etc. Longitude and latitude of GCPs can be determined by accurate base maps where maps are lacking GPS is used to determine the Latitude and Longitude from navigation satellites. Thus a GCP is located in the field and determing its position using GPS. Accurate GCPs are essential to accurate rectification. GCPs should be
Reliably matched between source and reference (e.g., coastline features, road intersection, etc.)
Widely disperced throughout the source image
Resampling Methods The location of output pixels derived from the ground control points (GCPs) is used to establish the geometry of the output image and its relationship to the input image. Difference between actual GCP location and their position in the image are used to determine the geometric transformation required to restore the image. This transformation can be done by different resampling methods where original pixels are resampled to match the geometric coordinates. Each resampling method employs a different strategy to estimate values at output grid for given known values for the input grid.
Nearest Neighbor The simplest strategy is simply to assign each corrected pixel, the value from the nearest uncorrected pixel. It has the advantages of simplicity and the ability to preserve original values in the altered scene, but it may create noticeable errors, which may be severe in linear features where the realignment of pixels is obvious. (Fig. 5).
Nearest Neighbor The simplest strategy is simply to assign each corrected pixel, the value from the nearest uncorrected pixel. It has the advantages of simplicity and the ability to preserve original values in the altered scene, but it may create noticeable errors, which may be severe in linear features where the realignment of pixels is obvious. (Fig. 5).
Bilinear Interpolation The strategy for the calculation of each output pixel value is based on a weighted average of the four nearest input pixels. The output image gives a natural look because each output value is based on several input values. There are some changes occurred when bilinear interpolation creates new pixel value. (Fig.6)
Brightness values in the input image are lost
As the output image is resampled by averaging over areas, it decreases the spatial resolution of the image
Cubic Convolution It is the most sophisticated and complex method of resampling. Cubic convolution uses a weighted average of values within a neighborhood of 25 adjacent pixels. The images produced by this method are generally more attractive but are drastically altered than nearest neighbor and bilinear interpolation.(Fig.7).
Image Correction using Mapping Polynomial Polynomial equations are used to convert the source coordinates to rectified coordinate, using 1st and 2nd order transformation . The coffiecients of the polynomial such as ai and bi are calculated by the least square regression method, that will help in relating any point in the map to its corresponding point in the image.
x0 = b1 + b2xi + b3yi
y0 = a1 + a2xi + a3yi
Where (xI yI ) are the input coordinates and (x0 y0 ) are the output coordinates.
Initially few GCPs cofficients are required to calculate the transformation matrix and the inverse transformation that could convert the reference coordinates of the GCPs back to the source coordinate system. This enables determination of RMS error for chosen transformation. The best order of transformation can be obtained using trial and error process while ignoring the highest RMS error from the least square computation.
Systematic Distortions
Geometric systematic distortions are those effects that are constant and can be predicted in advance. These are of two types:
Scan Skew
It is caused by forward motion of the spacecraft during the time of each mirror sweep. In this case the ground swath scanned is not normal to the ground track. (Fig.8).
Known Mirror Velocity Variation
The known mirror velocity variation are used to correct the minor distortion due to the velocity of the scan mirror not being constant from start to finish of each scan line. (Fig.9)
Cross Track Distortion
These generally occur in all the unrestored images accquired by the cross track scanners. They result from sampling pixels along a scan line at constant time intervals. The width of a pixel is proportional to the tangent of the scan angle and therefore is wider at the either margins of the scan line that compresses the pixel. This distortion is restored using trignometric functions.(Fig.10)
Systematic Distortions are well understood ands easily corrected by applying formulas derived by modelling the sources of distortions mathematically.
Atmospheric Corrections
The output from the instrument on satellite depends on the intensity and spectral distribution of energy that is received at the satellite. The intensity and spectral distribution of energy/radiation has traveled some distance through the atmosphere and accordingly has suffered both attenuation and augmentation in the course of journey. The problem comes whenone is not able to regenerate the correct radiation properties of the target body on the earth surface with the data generated by the remote sensing
Effect Of The Atmosphere on Radiation (Radiative Transfer Theory)
Fig.11. Effect of the atmosphere in determining various paths for energy to illuminate a pixel and reach the sensor The path radiation coming from the sun to the ground pixel and then being reflected to the sensor. In this on going process, absorption by atmospheric molecules takes place that converts incoming energy into heat. In particular, molecules of oxygen, carbon-di-oxide, ozone and water attenuate the radiation very strongly in certain wavelengths. Scattering by these atmospheric particles is also the dominant mechanism that leads to radiometric distortion in image data
Radiative Transfer theory is used to make quantitative calculations of the difference between the satellite received radiance and earth leaving radiance.
Radiation traveling in a certain direction is specified by the angle f between that direction and the vertical axis z and setting a differential equation for a small horizontal element of the transmitting medium (the atmosphere) with thickness dz. The resulting differential equation is called the radiative transfer equation. The equation will therefore be different for different wavelengths of electromagnetic radiation because of the different relative importance of different physical process at different wavelength.
Need for Atmospheric Correction
When an image is to be utilized, it is frequently necessary to make corrections in brightness and geometry for accuracy during interpretation and also some of the application may require correction to evaluate the image accurately. The various reason for which correction should be done:
Derive ratios in 2 bands of multi spectral image since the effect of atmospheric scattering depends on the wavelength, the two channels will be unequally affected and the computed ratio will not accurately reflect the true ratio leaving the earth''s surface
When land surface reflectance or sea surface temperature is to be determined.
When two images taken at different times and needed to be compared or mosaic the images
Correction Methods
Rectifying the image data for the degrading effects of the atmosphere entails modeling the scattering and absorption processes that take place. There are number of ways of correcting the image data for atmospheric correction
Ignore the atmosphere
Collecting the ground truth measurements of target temperature, reflectance etc and calibrating these values or quantities on the ground and the radiance values by the sensor.
Modeling the absorption or scattering effects for the measurement of the composition and temperature profile of the atmosphere.
Utilizing the information about the atmosphere inherent to remotely sensed data i.e use the image to correct itself.
Correcting For Atmospheric Scattering
This correction is done when the two bands of image are subjected to ratio analysis. Atmospheric scattering scatters short wavelength and causes haze and reduces the contrast ratio of images. This follows two techniques for example TM bands 1 & 7, where TM 1 has the highest component of 1 and the TM7 (infrared) has the least. Both techniques are DN value dependent as TM band 7 is free from scattering effect there it has DN value either 0 or 1 (shadows).
In TM 7 the shadows having DN value 0 & 1. Now for each pixel the DN in TM 7 is plotted against TM 1 and a straight line is fitted through the plot using least square techniques. If there was no haze in TM 1 then the line would pass through the origin. But as there is haze the intercept is offset along the band 1. Haze has an additive effect on scene brightness. Therefore to correct the haze effect on TM 1, the value of the intercept offset is subtracted from the DN of each band 1 pixel for the entire image.(Fig 12)
The second technique also uses the areas with DN as 0 or 1 in TM 7. The histogram of TM 7 has pixels with 0 where as the histogram of TM 1 lacks the pixel in the range from 0 to 20 approximately because of light scattered into the detector by atmosphere thus this abrupt increase in pixels in TM 1 is subtracted from all the DNs in band 1 to restore effects of atmospheric scattering.(Fig 13)
The amount of atmospheric correction depends upon
Wavelength of the bands
Atmospheric conditions
Short wavelength cause more severe scattering. Humid, smoggy and dusty cause more scattering than clear and dry atmospheres.
Implementing the Models
Documented information on the atmospheric conditions is used to estimate atmospheric using computer codes in standard Atmospheric Models. LOWTRAN, MODTRAN and HITRAN are some standard models providing them with type of sensor, target altitudes and look, the atmospheric correction could be done.
Image Enhancement Techniques
Image Enhancement techniques are instigated for making satellite imageries more informative and helping to achieve the goal of image interpretation. The term enhancement is used to mean the alteration of the appearance of an image in such a way that the information contained in that image is more readily interpreted visually in terms of a particular need. The image enhancement techniques are applied either to single-band images or separately to the individual bands of a multiband image set. These techniques can be categorized into two:
Spectral Enhancement Techniques
Multi-Spectral Enhancement Techniques
Spectral Enhancement Techniques
Density Slicing
Density Slicing is the mapping of a range of contiguous grey levels of a single band image to a point in the RGB color cube. The DNs of a given band are "sliced" into distinct classes. For example, for band 4 of a TM 8 bit image, we might divide the 0-255 continuous range into discrete intervals of 0-63, 64-127, 128-191 and 192-255. These four classes are displayed as four different grey levels. This kind of density slicing is often used in displaying temperature maps.
Contrast Stretching
The operating or dynamic , ranges of remote sensors are often designed with a variety of eventual data applications. For example for any particular area that is being imaged it is unlikely that the full dynamic range of sensor will be used and the corresponding image is dull and lacking in contrast or over bright. Landsat TM images can end up being used to study deserts, ice sheets, oceans, forests etc., requiring relatively low gain sensors to cope with the widely varying radiances upwelling from dark, bright , hot and cold targets. Consequently, it is unlikely that the full radiometric range of brand is utilised in an image of a particular area. The result is an image lacking in contrast - but by remapping the DN distribution to the full display capabilities of an image processing system, we can recover a beautiful image. Contrast Stretching can be displayed in three catagories:
Linear Contrast Stretch
This technique involves the translation of the image pixel values from the observed range DNmin to DNmax to the full range of the display device(generally 0-255, which is the range of values representable in an 8bit display devices)This technique can be applied to a single band, grey-scale image, where the image data are mapped to the display via all three colors LUTs.
It is not necessary to stretch between DNmax and DNmin - Inflection points for a linear contrast stretch from the 5th and 95th percentiles, or ± 2 standard deviations from the mean (for instance) of the histogram, or to cover the class of land cover of interest (e.g. water at expense of land or vice versa). It is also straightforward to have more than two inflection points in a linear stretch, yielding a piecewise linear stretch.
Histogram Equalisation
The underlying principle of histogram equalisation is straightforward and simple, it is assumed that each level in the displayed image should contain an approximately equal number of pixel values, so that the histogram of these displayed values is almost uniform (though not all 256 classes are necessarily occupied). The objective of the histogram equalisation is to spread the range of pixel values present in the input image over the full range of the display device.
Gaussian Stretch
This method of contrast enhancement is base upon the histogram of the pixel values is called a Gaussian stretch because it involves the fitting of the observed histogram to a normal or Gaussian histogram. It is defined as follow:
F(x) = (a/p)0.5 exp(-ax2)
