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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
OBJECT-BASED CHANGE DETECTION 
USING HIGH-RESOLUTION REMOTELY SENSED DATA AND GIS 
N. Sofina *, M. Ehlers ° 
‘University of Osnabrueck, Institute for Geoinformatics and Remote Sensing, 49076 Osnabrueck, Barbarastrasse 22b, 
Germany - nsofina@igf.uni-osnabrueck.de 
"University of Osnabrueck, Institute for Geoinformatics and Remote Sensing, 49076 Osnabrueck, Barbarastrasse 22b, 
Germany - manfred.ehlers@uos.de 
Commission VII, WG VII/5 
KEY WORDS: Change Detection, Geographic Information Systems (GIS), Detected Part of Contour, Generation of Features, Data 
Mining, GIS GRASS, Python 
ABSTRACT: 
High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which 
can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing 
remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same 
sensor which should be acquired at the same time of season, at the same time of a day, and — for electro-optical sensors - in cloudless 
conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. À promising alternative 
is the use of vector-based maps containing information about the original urban layout which can be related to a single image 
obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a 
consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed 
and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of 
building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' 
(DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects 
are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show 
the high reliability of the DPC feature as an indicator for change. 
1. INTRODUCTION 
Since the 20-th century the integration of cartography, 
Geoinformatics, and remote sensing technologies has been 
developed due to rapid technological advances. This evolution 
implied a number of derivative scientific directions and 
consolidation of conventional and digital cartographic 
techniques. 
The technological progress in remote sensing was one of the 
most important origins of this integration. Its influence on the 
cartography is many-folded. The main aspect is that remotely 
sensed images are considered as maps which lead to the 
development of new photogrammetry and image processing 
methods. 
A second origin of the integration tendencies was the 
development of geoinformation technologies. In recent years the 
application of GIS has offered the greatest potential for 
processing large volumes of multi-source data (Ehlers et al., 
1989). 
For change detection of the Earth’s surface the development of 
methods based on integrated analysis of vector and image data 
is a point of general interest. The majority of change detection 
methods address land-use change dynamics (see, for example, 
Centeno & Jorge, 2000; Lo & Shipman, 1990; Li, 2009; 
Mattikalli, 1995; Weng, 2002) and only a few deal with damage 
assessment. Samadzadegan  &  Rastiveisi (2008) used 
information from pre-event vector maps and post-event satellite 
images to compare different textural features for extracted 
building vector. Chesnel et al. (2007) used a pair of very high 
resolution images of a crisis region together with GIS data for 
investigation based on different acquisition angles of the 
images. 
This paper presents a GIS-based approach for the detection of 
destroyed buildings which were affected by a catastrophic event 
like an earthquake, a landslide or a military action. The 
methodology is based on the integrated analysis of vector data 
containing information about the original urban layout and 
remotely sensed images obtained after a catastrophic event. The 
proposed method was applied to damage mapping after the 
earthquake in Qinghai, China (2010). GIS information was 
obtained by digitizing from pre-earthquake images. The 
performed experiments indicate that a GIS-based analysis for 
change detection can essentially improve the potential of 
remotely sensed data interpretation and can be considered as a 
powerful tool for the detection of destroyed building. 
The proposed methodology was developed solely within the 
Open Source software environment (GRASS GIS, Python, 
Orange). The use of Open Source software provides an 
innovative, flexible and cost-effective solution for change 
detection analyses. 
2. STUDY AREA 
To assess the effectiveness of the proposed approach, the 
experiments were conducted using a high-resolution 
panchromatic QuickBird image acquired after the earthquake 
that struck Yushu, Qinghai, China on April 14, 2010 with a 
magnitude of 6.9Mw. GIS information was obtained by 
digitizing from pre-earthquake images.