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The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics
Chen, Jun

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001
Haigang SUI Deren LI Jianya GONG
Lab for Information Engineering in Surveying, Mapping And Remote Sensing (LIESMARS), Wuhan University, 129 Luoyu Road, Wuhan,
e-mail: shg@rcgis.wtusm.edu.cn
Keywords: remote sensing, change detection, change feature extraction
To enhance the ability of remote sensing system to provide accurate, timely, and complete geo-spatial information at regional or global
scale ,an automated change detection system has been and will continue to be one of the important yet challenging problem in remote
sensing. In this paper we designed a framework for automated change detection system at landscape level using various geo-spatial
data sources including multi-sensor remotely sensed imagery and ancillary data layers. In this framework database is the central part
and some associated techniques are discussed. These techniques includes five subsystems: automated feature-based image
registration, automated change identification, automated change feature extraction, intelligent change recognition, change accuracy
assessment and database updating and visualization.
1.Current change detection techniques
Automatic change detection in images of a given scene
acquired at different times is one of the most interesting topics
of image processing. It finds important applications within
different contexts, ranging from visual surveillance and video
coding to tracking of moving objects, from map updating to
environment monitoring[Rosin,1999].The basic assumption
behind change detection is that any changes on the ground
must result in changes in radiance values, so the changes
must be detected from noise by other factors ,such as
differences in atmosphere conditions .differences in
illumination condition, differences in relief condition
.differences in soil moisture and registration noise.
Usually, change detection involves the analysis of two
registered multi-spectral remote sensing images acquired in
the same geographical area at two different times. In the
remote sensing literature, two main approaches to the change-
detection problem have been proposed: the supervised and
the unsupervised approach. The former is based on
supervised classification methods, which require the
availability of a multi-temporal ground truth in order to derive a
suitable training set for the learning process of the classifiers.
The latter performs change detection by making a direct
comparison of the two images considered without relying on
any additional information.
Post Classification Comparison and Direct Multi-date
Classification are the commonly used techniques in the
supervised approach. Post Classification Comparison is the
most intuitive techniques in practice change detection, which
simply classifies the images of two times separately and
compares the classified maps on a pixel-by-pixel basis to
identify the changes. In contrast, the Direct Multi-date
Classification deals with the multi-spectral images of the two
times simultaneously. Each change combination between two
times is represented as a output class and the whole change
detection process is treated as one classification. The
supervised approach exhibits some advantages over the
unsupervised one(e.g., capability to explicitly recognize the
kinds of land cover transitions that have occurred .robustness
to the different atmospheric and light conditions at the two
acquisition times, ability to process multi-temporal and/or multi
sensor imagesj.The disadvantages include greater
computational and labeling requirements, severe difficulty in
obtaining individual classification accuracy and difficulties
inherent in performing adequate accuracy assessment on
historical data sets. Another drawback is the generation of an
appropriate multi-temporal ground truth is usually a difficult
and expensive task [Bruzzone et al.,2000].Consequently ,the
use of effective unsupervised change detection methods is
fundamental in many applications in which a ground truth is
not available.
The most widely used types of the unsupervised change
detection techniques is the so-called “difference image”. These
techniques involve image difference, image ratio, change
vector analysis(CVA) .principle component analysis(PCA) and
other methods such as neutral network .morphological