ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS", Bangkok, May 23-25, 2001
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AUTOMATIC REGISTRATION OF SATELLITE IMAGE TO MAP
Kensaku FUJII
NTT Cyber Space Laboratories
1-1 Hikarinooka Yokosuka-shi, Kanagawa 239-0847, Japan
Tel:+81-468-59-2431, Fax:+81-468-59-2829, E-mail:fujii@marsh. hil.ntt.co.jp
Tomohiko ARIKAWA
NTT Cyber Space Laboratories
KEY WORDS: Registration, Satellite image, Vector map, Normalized Difference Vegetation Index, Generalized Hough Transform
ABSTRACT
This paper introduces a fully automatic method for registering satellite images to vector maps. The key idea is to automatically match
the man-made objects extracted from satellite images against the corresponding objects in maps. The proposed method consists of
two procedures: feature extraction for matching, and mismatch determination.
Matching features are extracted using indicators derived from multi-spectral data of satellite images. The normalized difference
vegetation index is used as a spectral indicator. It is well known to provide the accurate differentiation of vegetation from man-made
objects.
In order to minimize registration mismatch, we apply the voting technique based on the generalized Hough transform. Voting is used to
judge the matching levels of displacement candidates. Mismatch is determined by voting using the positions of the features and those
of topographical objects projected onto satellite images.
Our approach is validated in experiments using actual multi-spectral date of satellite images and standard topological maps. The
experimental results show that the registration is extremely accurate; the registration mismatch is just a few pixels. This level of
performance confirms that our approach can automatically register satellite images against maps. To quantify the average
performance of the proposed approach, we also tested the approach of using image edges as matching features. These results reveal
that our approach yields high accuracy and small mean square error. It is clear that our approach can extract effective features from
satellite images because it makes good use of topographical reference objects through voting.
1. INTRODUCTION
The registration of airborne and spaceborne images plays a key
role in the field of photogrammetry. The demand to automate
such registration to maps is rapidly increasing in many
applications such as agriculture, town planning, map making
and updating, and disaster prevention.
Various sources can be used for GIS (Geographical Information
System) applications. A very recent source for these
applications is to use satellite images acquired by commercial
satellite systems (Space Imaging), (ORBIMAGE). Recent
progress in spaceborne sensing methods has made it possible
to be easily acquired large amounts of data that offer excellent
resolution (approximately a few meters). Therefore, satellite
images are now one of the most important sources in GIS.
Many approaches have been presented for the semi-automatic
or automatic interpretation of satellite images (Vogtle & Steinle,
2000), (Klang, 1997). With regard to registration to maps,
however, several problems remain.
The problems fall into two classes: problems with geometry
mismatch and problems with image processing, i.e. feature
extraction from satellite images. The former is mainly due to the
fact that satellite images have some errors in geometry caused
by mechanical device limitations or measurement error. With
the rapid development in remote sensing, highly accurate
geometry can be achieved by direct geo-referencing. However,
the best level of position alignment accuracy equals that of
1/25,000 scale maps without Ground Control Point (GCP)
correction. Since 1/2,500 scale maps are now being used as
GIS sources, the use of GCP will always be needed for
registration.
Due to the characteristic of satellite images, it is difficult to
process images i.e. feature extraction (Vohra & Dowman, 2000),
(Hild et at., 2000). Digital image processing becomes an
important tool for the quantitative and statistical analysis of
remotely sensed images (Ebner et al., 1999), (Grun &
Baltsavias, 1997). However, such images often contain
complex natural scenes and their resolution is at most a few
meters. This seriously impacts feature extraction, and
erroneous extraction seriously degrades registration accuracy.
It is necessary to achieve the robust interpretation of such
images.
This paper describes a fully automatic method for the
registration of satellite images to vector maps. The method
provides the automatic analysis of satellite images and objects
in maps, and completely eliminates the need for a priori
information. The general idea is to automatically match man
made objects extracted from satellite images against the
corresponding objects in maps. The proposed method consists
of two procedures: feature extraction for matching, and
mismatch determination. Our approach to identifying man
made objects is based on an indicator analysis of multi-spectral
data of satellite images. The normalized difference vegetation
index is used as a spectral indicator. It allows vegetation areas
to be differentiated from man-made objects (Schilling & Vogtle,
1996). Additionally, in order to minimize registration mismatch,
we apply the voting technique based on the generalized Hough
transform. We also present the results of automatic registration
experiments using actual satellite images and vector maps.
This paper is arranged as follows. Section 2 briefly explains the
procedure of matching feature extraction. Section 3 details the
process of determining registration mismatch. The results of
some experiments are presented in section 4. Finally, we
conclude this paper with future directions of our research.
2. MATCHING FEATURE EXTRACTION
This section describes our approach to extract matching
features from satellite images. The general idea is that the
features need to be correspond to map objects, which are
usually man-made objects, for example building, house and so
on. In detail, we use the NDVI (Normalized Difference
Vegetation Index), which is well known to accurately differentiate
