Image Processing Reference
In-Depth Information
9.4
Analysis Techniques
High resolution spectral data differ from multispectral data in their ability to detect
subtle differences in surface components. While other sensor concepts focus on the
utilization of different wavelength regions or fundamentally different acquisition tech-
niques (e.g. radar sensors or sounding sensors), high reso-
lution spectral data work in the same wavelength domains
as most multispectral devices, but in very narrow spectral
windows per band. As a consequence, the high number of
bands not only offers different analysis options, but actually
requires
different analysis techniques. While conventional
classification approaches may be utilized, comparable to
those employed for multispectral data analysis, the full
potential of such data is made accessible when more
sophisticated or adapted methods are utilized. In the fol-
lowing a focus is put on data optimization, classification/
material detection, and spectral mixture analysis.
Qualitative and
quantitative
analysis techniques
may be employed
with hyperspectral
data. The large
number of bands
may require a data
optimization to
retrieve optimum
results
9.4.1
Data Optimization
The high number of spectral bands can be regarded as an advantage and a problem
at the same time. A high spectral autocorrelation between neighboring wavelengths
leads to redundant information. Considering that hyperspectral data sets may easily
grow to GByte sizes, processing performance will unnecessarily suffer, depending
on hard- and software capabilities. While such problems will be overcome with
more powerful tools, the ability to derive useful information from such data sets
may also be impeded by redundant information. Data transformations are therefore
a standard pre-processing option in cases when the original spectral information is
not inevitably needed (e.g. for optimized classification).
The
Minimum
(or Maximum)
Noise Fraction
(MNF) is widely used to optimize
hyperspectral data analysis. Comparable to a principal component analysis, an MNF
transformation sorts the bands of a data set regarding variance explanation. It then
decorrelates the noise content in the data and orthogonalizes feature space (Green
et al.
1988
). The resulting MNF bands with low noise components may then be
analyzed during further processing steps (Fig.
9.4
).
Alternatively, the first bands that are considered to be noise-free may be extracted
and inverted again to yield noise-free reflectance data. It has to be remarked that
such a procedure has always to be considered in the light of the analysis goal.
Depending on the original feature space and the thematic question at hand, important
information may be found in less important MNF bands and a careful screening of
individual bands is necessary before either spectrally subsetting or inverting subsetted
data. In any case, a transformation of spectral library information is also mandatory
when using transformed data along with ground-based spectrometry.
