International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
PANSHARPENING OF HYPERSPECTRAL IMAGES IN URBAN AREAS
Chembe Chisense, Johannes Engels, Michael Hahn and Eberhard Gülch
Stuttgart University of Applied Sciences
Schellingstr. 24
D-70174 Stuttgart, Germany
johannes.engels@hft-stuttgart.de
Commission VII/6
KEY WORDS: image fusion, hyperspectral, resolution, urban, classification
ABSTRACT:
Pansharpening has proven to be a valuable method for resolution enhancement of multi-band images when spatially high-resolving
panchromatic images are available in addition. In principle, pansharpening can beneficially be applied to hyperspectral images as well.
But whereas the grey values of multi-spectral images comprise at most relative information about the registered intensities, calibrated
hyperspectral images are supposed to provide absolute reflectivity values of the respective material surfaces. This physical significance
of the hyperspectral data should be preserved within the pansharpening process as much as possible. In this paper we compare several
common pansharpening methods such as Principal Component Fusion, Wavelet Fusion, Gram-Schmidt transform and investigate their
applicability for hyperspectral data. Our focus is on the impact of the pansharpening on material classifications. Apart from applying
common quality measures, we compare the results of material classifications from hyperspectral data, which were pansharpened by
different methods. In addition we propose an alternative pansharpening method which is based on an initial segmentation of the
panchromatic image with an additional use of map vector data.
1 INTRODUCTION
Pansharpening is a well-established technique for the enhance-
ment of spatial image resolution, which allows the fusion of a low
resolution multiband image with a high resolution panchromatic
image. A mere upsampling of the multi-band image with an in-
terpolation filter would result in a blurry quality with smoothed
edges and missing short-wavelength constituents in the spatial
Fourier expansions. Therefore at least the short-wavelength spa-
tial information of the panchromatic image is integrated within
fusion. This process, however, entails quite an amount of arbi-
trariness. Whereas the overall brightness of a pixel (apart from
histogram matching) can be more or less directly adopted from
the panchromatic information, the ratio of the grey values of the
individual channels depends strongly on the respective pansharp-
ening method.
More recently developed sensors suggest the application of pan-
sharpening techniques, as those sensors frequently provide data
in different resolution levels. Even more significant is the differ-
ence between hyperspectral sensors and colour cameras. Hyper-
spectral data usually feature a lower resolution than RGB images.
However, pansharpening of hyperspectral imagery by a panchro-
matic image imposes two distinct problems:
e Often the ^panchromatic" image does not comprise the full
wavelength range of the hyperspectral image but only a part
of it. The panchromatic image might e.g. be derived from
RGB imagery, whereas the hyperspectral image covers a
wider range from the visible up to the shortwave infrared. In
such a case, the panchromatic image is not "representative",
i.e. not a pixelwise average of the hyperspectral image.
e Calibrated hyperspectral data ideally feature reflectivity val-
ues of the respective material surfaces (disregarding depen-
dencies on the source - reflector - sensor geometry, i.e. the
BRDF function). Therefore, in contrast to common imagery,
an absolute hyperspectral grey value carries physical signif-
icance as a distinctive parameter of the material surfaces
alone. Any "resolution enhancement" runs the risk of di-
luting this significance by a distortion of the parameter.
In the present paper we compare several common pansharpening
methods with respect to their impact or suitability for hyperspec-
tral data. We present in addition an alternative pansharpening
method, which is based on the segmentation of the panchromatic
image. The interpolation on the finer grid is performed by us-
ing data points from the same segment only. For the evaluation
we apply visual inspection, profile analysis and some common
quality measures. As more important, however, we rate a com-
parison of the classification results which are achieved from the
pansharpened images.
2 ESTABLISHED PANSHARPENING METHODS
In the last three decades, a lot of algorithms have been devel-
oped which are, however, based on only few elementary prin-
ciples. In any case, high-frequent panchromatic information is
merged into spatially low resolution but spectrally differentiated
data. We briefly outline the principles of some of the most com-
mon pansharpening methods. Here we follow the more detailed
representation in (Pohl and van Genderen 1998) or (Hirschmugl
et al. 2005).
2-1 Gram-Schmidt Fusion
The Gram-Schmidt Fusion works pixelwise. It was developed
and described in detail by (Laben and Brower 2000). In a first
step, a low resolution panchromatic channel is constructed as a
weighted average of the original hyperspectral channels. Based
on this first new channel, subsequently further linear combina-
tions are formed by orthogonalization of the original bands with
