ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 
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analysts have to manually process many critical tasks related 
to image processing, feature extraction and feature delineation 
and so on. There are many problems associated with these 
manual or semiautomatic process such as time-consuming, 
inconsistency, and being difficulty to apply them to large-scale 
and global information systems. Therefore, a reliable and 
automated change detection system is required. 
3.A framework for automatic change detection 
This research is designed for the development of a 
framework for automatic change detection. Based on this an 
automated spatial change information extraction system from 
various geo-spatial data sets will be created. This study has 
four interrelated subsystems as shown in Fig 1. In the first 
subsystem, we proposed to develop an automated image 
registration subsystem that registers images (or image to map) 
accurately and quickly with little or no operator supervision. 
After automatically registered images, a automated change 
finding subsystem, which takes multi-sensor and/or multi 
temporal, space-borne and/or air-borne, remotely sensed 
imagery or map and other data sources as its inputs, and 
finding changed area would be developed. In this subsystem, 
the output change information is divided into two groups: 
certain information (the changes with minimal uncertainties 
that can be easily extracted based on spectral and perhaps 
some other image characteristics) and uncertain information( 
the changes involving high level of uncertainties).The 
uncertain information is then presented to the second 
subsystem: automatic change feature extraction and 
identification subsystem in which changed image 
features(edge and area)are extracted. The changed 
information entered into the fourth subsystem: intelligent 
change recognition subsystem that is built upon knowledge- 
based techniques utilizing knowledge stored in the database. 
In this subsystem, the changed magnitude, changed type will 
be recognized based on some recognition algorithms. The 
procedures for reducing uncertainties including verification and 
accuracy assessment of both certain and uncertain changes 
are explicitly built into the proposed methodology. This is the 
fifth subsystem. Finally, the identified and recognized changed 
features are transmitted to information update system for data 
update and visualization for decision and policy-making 
purpose 
Fig 1 .A Framework for automated change detection system 
3.1 Automated image registration subsystem 
In automated change finding subsystem, the impact of 
misregistration on remotely-sensed change detection is 
quantitatively investigated and potential techniques to remove 
or reduce this impact is developed. Because of anticipated 
large data volume and high data rates of these current and 
future high-resolution sensors, the traditional approach of 
visual identification of tie-points to register multi-temporal and 
multi-sensor data is not an acceptable solution. The elements 
of this procedure include image segmentation, control point 
selection and correspondence, transformation parameter 
estimation and so on. An automated feature-based image 
registration procedure is one of the choices. In fact it includes 
two parts: one is a image-image registration procedure, 
another is a image-map registration. Knowledge about 
matching control points and recognition can be obtained from 
database. 
3.2 Automated change identification subsystem 
After accurate registration we use the image difference 
technique (one kind of widespread used change detection 
techniques) to get the difference image. The selection of 
threshold is automatic and the algorithm is suggested by 
Bruzzone (2000). Although because of the difficulty and 
uncertainties of threshold selection we can’t obtain the very 
precise changed area, it is possible to identify those areas with 
larger probability (this is certain change information) And those 
area with smaller probability would be divided into uncertain 
change information naturally. Obviously, this procedure is 
based on pixel level. 
3.3 Automated change feature extraction subsystem 
For certain and uncertain changed information especially 
uncertain information, the best way to discriminate them is to 
analyze further by additional information. The main features in 
images are edge and area that are the most important factors 
for object recognition. Although radiance values in images 
change at two different dates because of the differences in 
atmosphere conditions, illumination condition, relief condition, 
noise and so on, the basic shape and outline of features in 
images always keep invariant. So some image processing 
algorithms such as edge enhancement, edge detection, area 
segmentation and edge extraction are applied into the system. 
The goal of extracting a lot of edge and area image features 
lies in three aspects: one is to compare them between the two 
images for identify changed area, another is to provide image 
features to be recognized and the third is to update the 
database using changed image features. For those uncertain