nd -
ial
ly used
500 nm
accurate
yotential
icultural
thod are
the main
plied.
ays. The
ures and
he main
s, wheat,
vely and
| August,
e visual
elds and
' of more
verify the
)proqches
ruth data
ds.
alley test
| satellite
YPERION
n on the
ita (Level
essing of
correction
ion and
with two
70 bands
10.90 nm)
veen 852-
f the 242
ing Level
ION
IYPERION
:ene-based
er] and a
tion.
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
2.1 Striping Pixels Correction
Especially the first 12 VNIR bands and many SWIR bands of
HYPERION are influenced by striping. Stripes are caused by
calibration differences in the detector array, since the
HYPERION sensor acquires data in pushbroom mode with a
separate detector for each column and each band
[Goodenough et al, 2003]. Whereas several types of
abnormal pixels are corrected during Level IBI processing,
the case of intermittent striping pixels with lower DN values
compared to their neighboring pixels still exists. A
correction algorithm [Goodenough et al., 2003] to detect and
correct these pixels is applied to this study's dataset. The
algorithm traverses each band horizontally to compare each
pixel's DN value with the value of its immediate left and
right neighboring pixels. A pixel is labeled as a potential
abnormal pixel if its DN value is smaller than the DN's of
both neighbors. Afterwards, each band is traversed vertically
to count the number of consecutive potential abnormal
pixels in each column. Given the number of consecutive
potential abnormal pixels exceeds a user defined threshold
value and the percentage of abnormal pixels in a column is
also greater than a user defined threshold value, then the
column consists of abnormal pixels and it is marked as a
stripe. Finally, the abnormal pixels’ DN values are replaced
with the average DN values of the immediate left and right
neighboring pixels, assuming that nearby pixels have the
highest spatial autocorrelation with a center pixel
[Goodenough et al., 2003]. The abnormal pixel detection
algorithm performed well for most bands, except for
intensively striped ones.
Consecutively, an MNF transformation is applied to the
dataset in order to further segregate spatially structured
striping noise from the actual image data. A total of 15
transformed MNF-bands containing the coherent images are
retained in the data for the inverse MNF transformation. In
the end, 167 bands out of a total of 242 are chosen for
further processing and data analysis. Excluded bands
comprise those set to zero, bands in the overlap between the
two spectrometers, and bands affected by atmospheric water
vapour and heavy noise, which can easily be identified by
visual inspection of the image data [Datt et al., 2003].
2.2 Atmospheric Correction
Atmospheric correction of the generated 167 channel
HYPERION dataset is performed using ATCOR-4 [Richter,
2003], an atmospheric correction program based on look-up
tables generated with a radiative transfer code (MODTRAN-
4). An inflight calibration approach based on two targets in
the scene is chosen with in-situ measured spectroradiometric
ground-truth data (ASD FieldSpec Pro FR
Spectroradiometer). The composition of the atmosphere is
characterized assuming a typical Limpach Valley summer
scenario in terms of water vapour content, aerosol type and
optical thickness during HYPERION data take on August 18,
2002.
2.3 Geometric Correction
For orthorectification of the HYPERION scene, the software
package PCI Geomatica OrthoEngine is chosen, which uses
the parametric sensor model [Toutin, 1985]. The dataset
with its 167 bands has to be divided into 5 smaller datasets
due to software restrictions, consisting of about 22 bands,
which are imported with the corresponding orbit and scene
parameters. 26 ground control points (GCPs) and 9
independent check points (ICPs) are collected from a
previously orthorectified SPOT 4 scene and transferred to all
datasets. The root mean square (RMS) residuals of the GCPs
867
from the CCRS parametric model are 6.4 m in X and 8.4 m in
Y directions, respectively. The RMS errors of the ICPs are 8.3
m and 15.4 m in X and Y directions, respectively. For
orthorectification the 25-m pixel spacing digital terrain
model (DTM) of Switzerland, DHM25, is applied. The nearest
neighbor resampling method is used to preserve the blocky
structure of the agricultural fields and the original
radiometry of the image.
+ 3. METHODOLOGY
Land use classification from HYPERION data in the Limpach
Valley is performed based on both a pixel-oriented
classification approach (Spectral Angle Mapper, SAM) and a
multiscale object-oriented method, which bears the potential
to classify pixels not only based on their spectral
information, but also by their texture and local context. The
results of the two methods are discussed and compared and
an accuracy assessment is performed. The accuracies are
determined through a pixel to pixel comparison and
expressed as overall, producer, user and inclass accuracy
[Story & Congalton, 1986].
3.1 Spectral Angle Mapper (SAM)
The Spectral Angle Mapper [Boardman & Kruse, 1994] is a
technique to classify hyperspectral data by determining the
similarity between an endmember spectrum (considered as
an n-dimensional vector, where n is the number of bands)
and a pixel spectrum in an n-dimensional space. Smaller
angles represent closer matches to tlie reference spectrum.
Since this method uses only the direction of a vector and not
its length, it is insensitive to illumination and albedo
effects. Image-based endmember spectra of the main
agricultural land use types in the test area are used as input
for Spectral Angle Mapper classification.
3.2 Multiscale Object-Oriented Approach
eCognition! software is the first commercially available
product for object-oriented and multi-scale image analysis.
As opposed to most other pattern recognition algorithms
which operate on a pixel-by-pixel basis, eCognition
segments a multispectral image into homogeneous objects,
or regions, based on neighboring pixels and spectral and
spatial properties. The segmentation algorithm does not
only rely on the single pixel value, but also on pixel spatial
continuity (texture, topology). The resulting formatted
objects have not only the value and statistic information of
the pixels that they consist of but also carry texture, form
(spatial features) and topology information in a common
attribute table. The user interacts with the procedure and,
based on statistics, texture, form and mutual relations
among objects, defines training areas. Image segmentation
can be performed at different levels of resolution, or
granularity as seen in Figure 1. It is controlled by a user-
defined threshold called scale parameter. A higher scale
parameter will allow more merging and consequently bigger
objects, and vice versa. The homogeneity criterion is a
combination of color respectively spectral values and shape
properties (shape splits up in smoothness and compactness).
By applying different scale parameters and color/shape
combinations, the user is able to create a hierarchical
network of image objects. For the classification of the
segments, two types of nearest neighbor expressions can be
used in eCognition: the nearest neighbor (NN) and the
standard nearest neighbor (Std. NN) expression. The NN
expression and its feature space can be individually
! Definiens AG, Trappentreustr. 1, 80339 Munich, Germany
