to effectively detect sea surface temperature trends (its primary 
mission aim) throughout its mission and a dual view to correct 
for 
   
  
  
Figure 1 (a-c). The top image (Figure 1a.) is the 0.55um nadir view from AATSR orbit 33086 (28* June 2008). The 
image is of the Eastern coast of Greenland, the orange arrow represents the direction of motion. The middle image 
(Figure 1b.) is the raw CTH output from the Census stereo matching between the nadir and forward views, with red 
denoting the height at sea-level and blue and dark blue a value of around 8km. The bottom image (Figure 1c.) is the 
CTH after application of a 5x5 median filter. The black transect passing through the image is the path of the 
CALIOP overpass used for the validation. 
atmospheric effects. Following on from ATSR were the ATSR- 
2 instrument launched onboard ERS-2 in 1995 (with three 
additional visible bands at 0.55, 0.65 and 0.87um) and the 
AATSR instrument onboard Envisat in 2001 (with 12 bit 
radiometric resolution). The instrument employs conical 
scanning and has two views one at nadir and one forward at an 
incidence angle of 55, which allows the use of 
photogrammetric techniques. The swath width is 512km and is 
resampled to give a nominal product resolution of 1km. 
The potential for Stereoscopic CTH determination from the 
(A)ATSR(-2) instruments was first proposed prior to the launch 
of ATSR (Lorenz, 1985). With accurate knowledge of the 
(A)ATSR(-2) viewing geometry and a method to determine 
parallax, hereafter referred to as disparity, by matching of pixels 
in the along track direction between the two views, CTH can be 
retrieved. A technique for doing so was first implemented on 
ATSR imagery by Prata and Turner (1997), using a simple 
patch based correlation matching metric. A camera model 
described in the same paper was then applied to convert the 
parallax measurements into CTHs. More recently a far more 
sophisticated patch-based algorithm, referred to as M4 (Muller 
et al, 2007), with an improved (A)ATSR(-2) camera model 
(Denis et al., 2007), has been developed. 
2.2 M4 
The M4 stereo matching algorithm was developed for extensive 
processing of ATSR-2 data for the EU-CloudMap (Muller and 
Fischer, 2007). With a shared heritage to the M2/M3 matchers 
applied to MISR (Muller et al., 2002, Moroney et al., 2002), 
M4 achieves significant performance gains over it predecessors 
through application of advanced image processing techniques. 
The matching metric applied in M4 is normalised cross 
correlation (NCC). NCC is an excellent metric as it can account 
for gain differences between images and is the optimal method 
for dealing with Gaussian noise typically present in imagery 
(Hisrchmuller et al, 2002). Therefore it is robust any changes 
in illumination caused by the different view angles and noise 
effects caused by the different resolutions of the ATSR images. 
The effectiveness of NCC in providing effective disparity 
estimates deteriorates in the presence of depth discontinuities. A 
depth discontinuity is the change from one disparity population 
to another, from the Earth’s surface to a cloud, for example. 
These depth discontinuities typically present as intensity 
   
   
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