New image processing tools for GRASS

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