New image processing tools for GRASS

Markus Neteler
ITC-irst, Trento, Italy

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     Last modified: July 30, 2004
     Presentation date: 09/13/2004 9:00 AM in FE
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abstracts
In this paper we present a suite of new image processing tools for
GRASS. These new programs provide support for image geocoding and
image fusion. Moreover, multi- and hyperspectral image analysis has
been implemented to derive landuse/landcover maps at subpixel
resolution.

PART I

The module 'i.linespoints' allows for image registration
by defining ground control points as well as corresponding lines.
The integration of lines into the registration procedure supports
accelerated and simplified search of corresponding structures in
source and target images. The resulting table of ground control points
is provided as input to the new rectification tool 'i.homography'.

A new module 'i.coregister' provides an alternative semiautomated
approach to find corresponding points in two overlapping images.
In order to obtain a good registration accuracy, first two regions
are roughly indicated on screeen, with very general requirements to
image dimensions and overlapping zone characteristics. Given the
matching region, the algorithm defines dynamic search windows and
computes the cross-correlation function within subwindows. Based on
the Fast Fourier Transform, the maximum correlation value delivers
the positions of the GCPs, which are saved into the common POINTS
structure for later use with 'i.rectify'. The list of GCPs created by
above modules can optionally be converted into the POINTS structure
of 'i.ortho.photo' by a new script 'i.points2orthophoto.sh'.

A new application of the 'i.ortho.photo' algorithm is proposed for
the registration of oblique imagery as produced by hand-held digital
cameras. The underlying idea is to improve the visual perception
of perspective rendering based on orthophotos. While oblique rendering
using a digital elevation model and orthophotos usually suffers from
perspective displacements, we show that digital photos even taken
by a cheap digital camera can be geocoded and used to improve the
visual impression.


PART II

In the next part of this paper, we present two methods related to
multi- and hyperspectral cameras. Spectral angle mapping has been
implemented in the new module 'i.spectral.sam'. The algorithm is
calculating for a set of bands the angles to a set of object spectra
read from a spectral library.

Spectral unmixing for landuse/landcover mapping at subpixel precision
has been implemented in the module 'i.spectral.unmix'. Multi- and
hyperspectral data can be analysed against a spectral library. Instead
of single resulting map as received from common classification algorithms,
here as many abundance maps as object spectra are generated.

A new script 'i.fusion.brovey' has been written to support PAN sharpening
of multispectral satellites such as LANDSAT-7, QuickBird and SPOT. The
algorithm performs Brovey transform image fusion of the high resolution
panchromatic channel with the multispectral channels at lower resolution.

Finally, we will show a high performance solution for image classification
in GRASS at meso-scale and high spatial resolution. A script-based
approach to run standard GRASS on an openMosix cluster (20 PCs, 40 CPUs)
has been implemented to classify multispectral color orthophotos with SMAP
algorithm. The study area covers approximately 6200 square kilometers,
the resolution of the orthophotos is at one meter per pixel. In tests, the
required time to analyse 280 orthophotos at the given resolution was
reduced from estimated 118 days on a single CPU to 5 days on the
openMosix cluster.