List of Tables
3. THE THEORY OF IMAGE PROCESSING AND ANALYSIS
3.3 ATMOSPHERIC CORRECTION METHODS
3.3.2 Relative atmospheric corrections
Relative correction techniques are particularly relevant to the correction of multitemporal images. These correction procedures correct multiple images relative to a selected reference image so that they appear to have been obtained under the same atmospheric conditions as the reference image.
A number of different methods of relative image normalisation have been described in the literature. One of the earlier methods was presented by Schott et al. (1988) who developed the procedure in order to facilitate the analysis of multidate imagery. In presenting their technique, Schott et al. (1988) explained that trying to compare multiple images of the same area was often problematic due to changes in illumination, atmospheric conditions
and sensor characteristics over the various image acquisition dates. Their technique required the identification of targets of constant reflectivity across the various images, targets they called pseudoinvariant features (PIF). These targets were typically man-made features whose reflectivity was almost constant and which were assumed to be the same across all the images. Any differences in the satellite-measured reflectivity of these targets on different dates was assumed to be due to atmospheric effects, changes in viewing geometry and/or sensor characteristics. Schott et al. (1988) identified pseudoinvariant targets by means of an image segmentation technique that excluded all pixels not representing urban features. The selected target pixels were then used to calculate linear transformation formulas which, when applied to the satellite images, radiometrically normalised them relative to a selected reference image. Schott et al. (1988) cautioned that their technique was sensitive to changes in surface moisture, and that features that were assumed to be invariant might in fact have a different reflectivity when wet.
A slightly different image normalisation technique was developed by Eckhardt et al.
(1990). As with the Schott et al. (1988) method, their technique, which Eckhardt et al.
(1990) called empirical scene normalisation, required the identification of normalisation targets. However, targets were not identified using an image segmentation approach but were selected through visual inspection of the images. Eckhardt et al. (1990) listed a number of criteria for potential normalisation targets:
• the targets had to be at approximately the same elevation as the features which were to be analysed on the images
• there should be little or no vegetation on the targets
• the targets needed to be relatively flat
• the patterns on the targets should be the same and should not change over time Targets representing a wide range of brightness or reflectivity values were chosen.
Typically these would include dark targets like deep water bodies and bright targets like bare soil, and were assumed to be constant reflectors. Eckhardt et al. (1990) reported correlation coefficients (expressed as R2 values) of greater than 0.99 when relating target pixel values from the reference image to those from the images being normalised, verifying their assumption that the selected target areas were constant reflectors. Using the pixel values of the target areas, a series of normalisation regression equations were developed and applied to the images being normalised. Eckhardt et al. (1990) explained that these
regression equations, which were of the form y = mx + c, contained an additive component, c, which corrected for differences in path radiance between the different image dates, and a multiplicative component, m, which corrected for differences in the sensor calibration, sun angle, earth/sun distance and atmospheric attenuation.
A third method of relative radiometric normalisation was proposed by Hall et al. (1991).
Their aim in developing the technique was to radiometrically rectify a series of images so that it would appear '..as if they were acquired with the same sensor, while observing through the atmospheric and illumination conditions of the reference image'. The authors' rationale for doing this was that it was very hard, if not impossible, to obtain measurements of atmospheric properties at the time of image acquisition. This made it extremely difficult to remove atmospheric effects across multidate images. The method proposed by Hall et al. (1991) was similar to the previous two methods in that target pixels were used to establish a relationship between a reference image and the images to be normalised. Where their method differed was in the method of target selection. They proposed using the extremes of a Kauth-Thomas greenness-brightness scattergram3 to identify non-vegetated target areas. According to Hall et al. (1991), these non-vegetated extremes of the scattergram did not necessarily represent the same pixels across the images but instead represented non-vegetated areas of similar surface material. They identified two target areas on the scattergram, a dark radiometric control set comprising areas of deep water, and a bright control set made up of rocky outcrops and areas of concrete. Using pixels from the target areas, a series of linear transformations were generated which established a relationship between the reference image and subject images. Hall et al. (1991) noted that the accuracy of their technique could be compromised by (a) heterogeneity in the atmosphere across the scene, (b) non-linearity between the sensor calibrations, and (c) non- linear distortions of the scattergram due to the effects of rainfall.
A number of multitemporal studies have used relative radiometric correction in order to standardise multidate images. Jensen et al. (1995), in producing an inventory of cattail and sawgrass in parts of the Florida Everglades, used a slight variation of the Eckhardt et al.
(1990) method. They selected areas of water (wet) and unvegetated bare soil (dry) as normalisation targets. Their variation resulted from the fact that the selected dry targets
3. Kauth & Thomas (1976) developed the Tasselled Cap transformation, a transformation technique that extracted brightness, greenness and wetness components from a series of multispectral Landsat images.
shifted from date to date and hence the pixels in these targets did not represent the same ground location on all the images. Apart from this, their application of the method was as outlined in Eckhardt et al. (1990).
Munyati (2000), in a study of changes occurring in parts of the Kafue River floodplain, also used the empirical scene normalisation method of Eckhardt et al. (1990). A number of target areas were identified on the images, with irrigation reservoirs representing the wet target areas and unvegetated bare soil the dry targets. Munyati (2000) reported R2 values of above 0.99 when normalising Landsat Thematic Mapper images to the reference image (also a Landsat TM image) but obtained lower R2 values (e.g. 0.91) when relating Landsat MSS images to the reference image. This was most likely due to differences in the spectral and spatial resolution between the multispectral and thematic mapper scanners. In another change-detection study, Roberts et al. (1999) used the pseudoinvariant feature technique developed by Schott et al. (1988). A variation they employed was to first convert the reference image to surface reflectance by using signatures from a library of reflectance spectra. An advantage of this variation was that subject images normalised relative to this reference image would then also be calibrated in terms of surface reflectance.
The various relative radiometric correction techniques described above, whether using pseudoinvariant targets, dry/wet targets or bright/dark targets all had the same aim, namely to normalise a series of target images relative to a selected reference image so that all the images appeared to have been obtained under the same atmospheric conditions and with the same sensor. These techniques have been successfully applied in a number of studies and have the advantage of being relatively easy to implement. In addition, if the reference image is converted to values of surface reflectance, then relative normalisation of the target images will convert them to surface reflectance too.