CHAPTER 7
MI Based Registration of Multi-Sensor
and Multi-Temporal Images
Hua-mei Chen, Pramod K. Varshney
7.1
Introduction
The necessity of accurate registration and geometric rectification arises due
to the presence of a number of distortions (errors) in remote sensing images
that occur as a result of variations in platform positions, rotation of earth
and relief displacements etc. (see Chap. 2). The behavior of most of these
distortions is systematic and, thus, can be easily removed at data acquisition
centers. It is also generally expedient to procure systematic-corrected images
as these are already geometrically rectified to a map projection system such
as the Universal Transverse Mercator (UTM). However, systematic corrections
are normally performed on the basis of platform ephemeris data obtained
from the header information, which may be relatively inaccurate. Therefore,
some distortions may still be present in the systematic-corrected data. This
can be illustrated with the help of agency supplied Landsat TM images taken
at two different times as shown in Fig. 7.1. The images were systematically
corrected and geometrically rectified to UTM at the data acquisition center. It
can, however, be seen that the two points (marked by "+") having the same
UTM coordinates in both images are located at different positions indicating
the presence of non-systematic registration errors.
Traditionally, image registration for remote sensing applications is performed by selecting a few features, also known as ground control points (GCP),
in both images. The GCP are matched by pair and transformation parameters
to register the images are computed (see Chap. 2 for details). A resampling
procedure is then employed to estimate the intensity values at the new sampled positions of the reference image. The feature based registration technique
via manual selection of GCP is, however, laborious, time intensive and a complex task. Some automatic algorithms have been developed to automate the
selection of GCP to improve efficiency (Toth and Schenk 1992; Dai and Khorram 1999). However, the extraction of GCP may still suffer from the fact that
sometimes too few a points will be selected, and the extracted points may be
inaccurate and unevenly distributed over the images. This may lead to large
registration errors. On the other hand, intensity-based registration techniques
do not involve feature identification and extraction. Hence, automatic intensity based registration techniques may be more appropriate than the feature
P. K. Varshney et al., Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data
© Springer-Verlag Berlin Heidelberg 2004
MI Based Registration of Multi-Sensor
and Multi-Temporal Images
Hua-mei Chen, Pramod K. Varshney
7.1
Introduction
The necessity of accurate registration and geometric rectification arises due
to the presence of a number of distortions (errors) in remote sensing images
that occur as a result of variations in platform positions, rotation of earth
and relief displacements etc. (see Chap. 2). The behavior of most of these
distortions is systematic and, thus, can be easily removed at data acquisition
centers. It is also generally expedient to procure systematic-corrected images
as these are already geometrically rectified to a map projection system such
as the Universal Transverse Mercator (UTM). However, systematic corrections
are normally performed on the basis of platform ephemeris data obtained
from the header information, which may be relatively inaccurate. Therefore,
some distortions may still be present in the systematic-corrected data. This
can be illustrated with the help of agency supplied Landsat TM images taken
at two different times as shown in Fig. 7.1. The images were systematically
corrected and geometrically rectified to UTM at the data acquisition center. It
can, however, be seen that the two points (marked by "+") having the same
UTM coordinates in both images are located at different positions indicating
the presence of non-systematic registration errors.
Traditionally, image registration for remote sensing applications is performed by selecting a few features, also known as ground control points (GCP),
in both images. The GCP are matched by pair and transformation parameters
to register the images are computed (see Chap. 2 for details). A resampling
procedure is then employed to estimate the intensity values at the new sampled positions of the reference image. The feature based registration technique
via manual selection of GCP is, however, laborious, time intensive and a complex task. Some automatic algorithms have been developed to automate the
selection of GCP to improve efficiency (Toth and Schenk 1992; Dai and Khorram 1999). However, the extraction of GCP may still suffer from the fact that
sometimes too few a points will be selected, and the extracted points may be
inaccurate and unevenly distributed over the images. This may lead to large
registration errors. On the other hand, intensity-based registration techniques
do not involve feature identification and extraction. Hence, automatic intensity based registration techniques may be more appropriate than the feature
P. K. Varshney et al., Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data
© Springer-Verlag Berlin Heidelberg 2004