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LANDSAT-SPOT DIGITAL IMAGES INTEGRATION USING
GEOSTATISTICAL COSIMULATION TECHNIQUES
a *
J. Delgado* , A. Soares”, J. Carvalho”
" Cartographical, Geodetical and Photogrammetric Engineering Dept.,
University of Jaén, c/ Virgen de la Cabeza, 2 — 23071 Jaén, Spain — jdelgado@ujaen.es
? Environmental Group of the Centre for Modelling Petroleum Reservoirs, CMRP/IST,
Av. Rovisco Pais, 1049-001 Lisbon, Portugal — nermp@alfa.ist.utl.pt, jcarvalho@ist.utl.pt
KEY WORDS: Remote Sensing, Satellite, Multiresolution, Integration, Algorithms
ABSTRACT:
Interpretation of remote sensing images into terrestrial attributes is very dependent of their spatial and spectral resolution. Normally,
these types of resolution are contradictory: high spatial resolution sensors have a low spectral resolution whereas multispectral
sensors have a low spatial resolution. Digital image-merging procedures are techniques that aim at integrating the multispectral
characteristics in a high spatial resolution image. The main objective is to obtain synthetic images that combine the advantages of
the high spatial resolution and high spectral resolution of both types of images. Unfortunately, the most commonly used methods can
not be considered real merging methods. They consist in a simple substitution of the high-spectral images with a high-spatial
resolution image based on the correlation between both data sets. The images obtained by those merging/substitution procedures,
although honouring the values of multispectral images, do not account for the spatial patterns of high spatial resolution images. In
this paper a new merging approach is presented. The method is based on a geostatistical technique of direct sequential cosimulation
that aims at producing images with the spatial patterns of high spatial resolution images and the local values of the coarse
multispectral images. The method was applied to Landsat-TM and SPOT-P images and the results were compared with the images
provided by other common merging procedures. Using the proposed geostatistical procedure, the merged images preserve the
spectral characteristics of the higher-spectral resolution images in terms of both descriptive statistics and band correlation
coefficients.
1. INTRODUCTION
Digital images are very frequently used in environmental and
cartographic applications. Nowadays there is a wide range of
systems that provide environmental and cartographic images
in digital format. These images are classified according to
their spatial and spectral resolution. Unfortunately, in most
cases, these resolutions do not match. The high-spatial-
resolution sensors have a low spectral resolution whereas the
multispectral sensors have a good spectral resolution but a
bad spatial resolution that limits their use in some detailed
environmental applications (Figure 1).
et
m Nr un : TE T
es im. = i Ba i [I
Figure 1. Left: SPOT-P image (GSD=10m),
Right: TM543 image (GSD=30m)
This problem is solved using digital image merging
procedures. The main objective of these methods is to obtain
Synthetic images that combine the advantage of the high
Spatial resolution of one image with the high spectral
895
resolution of another one. These merged images have
important environmental applications like land-use,
vegetation and lithological mapping, and process
monitorization (for example, pollution control); applications
that need to combine multispectral information with a good
spectral resolution that allows the cartographical product
generation at adequate scales.
Ideally, the method used to merge data sets with high-spatial
resolution and high-spectral resolution should not distort the
spectral characteristics of the high spectral resolution data.
Not distorting the spectral characteristics is important for
calibrating purposes and to ensure that targets that are
spectrally separable in the original data are still separable in
the merged data set (Chavez et al., 1991).
The objective of this paper is to present the results of a
geostatistical merging image methodology based on direct
sequential simulation. The method is used to merge the
information contents of 30m Landsat- TM and 10 m SPOT-P.
2. MERGING PROCEDURES
2.1 Classical merging procedures
In order to compare results two non-geostatistical image
merging methods were applied: a) Hue-Saturation-Value
transformation and b) Colour Normalized.
