Vol. XXXVIII, Part 7B 
In: Wagner W., Szekely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
105 
:tober 31, 2009; 10:00 
5 solar zenith, 137.6 0 
s the lOlOnm band, C 
TOnm) interpreted as 
effect have applicable 
and processing that is 
Dual images, acquired 
ed campaigns. Three 
illow: 1) enhancing 
kV, and 3) performing a 
ie-shadowing process 
ge's statistics to gain 
' to correct the shadow 
data sets acquired on 
ocess is missing in the 
ing algorithm consists 
teps: an atmospheric 
isking, five additional 
iriance matrixes and 
task, and final step is a 
to the pixels in the 
ping shadow areas in 
An interpretation of 
ow areas with highly 
l' for each pixel in the 
e provides an external 
i of ATCOR-4, allows 
fication. The proposed 
y tested on six scenes 
ltage of the presented 
operator, and it is fast 
on the boresight ratio 
Figure 3. Shadow masks of - A is an AISA-Dual image 
(December 18, 2009; 11:30 GMT; 15km visibility; midlatitude 
winter model; 60 0 solar zenith; 205.8 0 azimuth angle), B is a 
boresight shadow mask, C is a boresight shadow estimation 
(yellow 0.1-20%, cyan 20-50%, magenta 50-80% and maroon 
80-100%), D is an ATCOR-4 shadow mask 
3.2 Stereo 3-D map 
The simultaneous across-track stereo-data acquisition gives a 
strong advantage in terms of radiometric variations. Since an 
error of ±3 pixel along-track and ±1 pixels across-track for the 
parallax measurements in the automated matching process has 
been achieved with these different datasets (along-track and 
across-track), the potential accuracy for the across-track stereo- 
derived local DEM from AISA-Dual could be on the order of 3 
m (1.5x1.5 pixels) . The main objectives of this application are 
to generate and evaluate pixel based local DEMs from the 
boresight VNIR and SWIR images. The 3-D stereo intersection 
is performed using a computed geometric model to convert the 
pixel coordinates in both images determined in the image 
matching of the stereo pair to 3-D data. The non earth 
coordinates are determined for the measured point with a least 
square intersection process based on the geometric model 
equations and parameters (Toutin, 1995). The result is an 
irregular grid in the map projection system, which is 
transformed to a raw regular DEM. 
Figure 4. DEM, A is the AISA-Dual image (October 122, 2006; 
08:55 GMT; midlalitude summer model; 46.4 0 solar zenith; 
164.6 0 azimuth angle), B is the 3-D stereo model, C is the 
boresight calculated band, D is the raw DEM (cloud masked- 
black border) modeled with Inverse Distance Weighting (IDW) 
algorithm (0m 30m), E is the AISA-Dual in 3-D view 
(x axis 1500m, y axis 500m, z axis 30m) 
3.3 Unmixing and anomaly detection 
Most of the pixels collected by HRS airborne sensors contain 
mixed spectra from the reflected surface radiation of various 
materials in the sub-pixels. As a result, mixed pixels may exist 
when the spatial resolution of the sensor is not sufficient to 
separate different pure signature classes. The resulting spectral 
measurement is a composite of the individual pure spectra 
weighted by a set of scalar endmember-abundance fractions 
(Adams et al., 1986). Under such circumstances, target 
detection must be carried out at sub-pixel level. An anomaly 
detector enables to detect targets whose signatures are spectrally 
distinct from their surroundings with no a priori knowledge. In 
general, such anomalous targets are relatively small compared 
to the image background and only occur in the image with low 
probabilities. Two approaches are of particular interest. One 
was developed by Reed and Yu (Reed and Yu, 1990; Yu et al., 
1993; Yu et al., 1997) and is referred to as the RX detector 
(RXD). The RX detects spatial/spectral anomalies using the 
sample covariance matrix to detect "interesting target" pixels 
which occur with low probabilities in the data (i.e., the size of 
target samples is small) compared to Gaussian distribution of 
the background. Second is a SVDD, (support vector approach) 
that is a non-parametric method with several benefits, including 
non-Gaussian modeling basis that can model arbitrarily shaped 
and multi-model distributions, scarcity and high generalization 
ability (Baneijee et al., 2006). 
We suggest using boresight calculated band as spectral/spatial 
anomaly detector. Since an anomaly defined as a small target 
with distinct spectrum, an accurate pixel-by-pixel classification 
of boresight values may accentuate the targets in question.