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 
    
CEST ANALYSIS: AUTOMATED CHANGE DETECTION FROM 
VERY-HIGH-RESOLUTION REMOTE SENSING IMAGES 
Manfred Ehlers®, Sascha Klonus*, Thomas Jarmer“, Natalia Sofina*, Ulrich Michel?, Peter Reinartz°, Beril Sirmacek® 
? Institute for Geoinformatics and Remote Sensing, University of Osnabrueck, 49076 Osnabrueck, Germany 
(mehlers, sklonus, tjarmer, nsofina)@igf.uni-osnabrueck.de 
^ University of Education, Department of Geography, Heidelberg, Germany (e-mail: michel(g)ph-heidelberg.de) 
* German Aerospace Center DLR, Remote Sensing Technology Institute, Wessling, Germany (e-mail: peter.reinartz, 
beril.sirmacek@dlr.de) 
KEY WORDS: Change Detection, Disaster, Texture, Visualization, Principal Component Analysis 
ABSTRACT 
A fast detection, visualization and assessment of change in areas of crisis or catastrophes are important requirements for coordination 
and planning of help. Through the availability of new satellites and/or airborne sensors with very high spatial resolutions (e.g., 
WorldView, GeoEye) new remote sensing data are available for a better detection, delineation and visualization of change. For 
automated change detection, a large number of algorithms has been proposed and developed. From previous studies, however, it is 
evident that to-date no single algorithm has the potential for being a reliable change detector for all possible scenarios. This paper 
introduces the Combined Edge Segment Texture (CEST) analysis, a decision-tree based cooperative suite of algorithms for 
automated change detection that is especially designed for the generation of new satellites with very high spatial resolution. The 
method incorporates frequency based filtering, texture analysis, and image segmentation techniques. For the frequency analysis, 
different band pass filters can be applied to identify the relevant frequency information for change detection. After transforming the 
multitemporal images via a fast Fourier transform (FFT) and applying the most suitable band pass filter, different methods are 
available to extract changed structures: differencing and correlation in the frequency domain and correlation and edge detection in 
the spatial domain. Best results are obtained using edge extraction. For the texture analysis, different *Haralick' parameters can be 
calculated (e.g., energy, correlation, contrast, inverse distance moment) with ‘energy’ so far providing the most accurate results. 
These algorithms are combined with a prior segmentation of the image data as well as with morphological operations for a final 
binary change result. A rule-based combination (CEST) of the change algorithms is applied to calculate the probability of change for 
a particular location. CEST was tested with high-resolution satellite images of the crisis areas of Darfur (Sudan). CEST results are 
compared with a number of standard algorithms for automated change detection such as image difference, image ratioe, principal 
component analysis, delta cue technique and post classification change detection. The new combined method shows superior results 
averaging between 4596 and 1596 improvement in accuracy. 
1. INTRODUCTION 
For change detection from remotely sensed images many 
methods have been proposed and developed. An overview and 
comparison of different change detection methods can be found 
in Singh (1989); Lu et al. (2003); or Coppin et al. (2004). In 
generally, change detection methods can be divided into three 
categories (Mas 1999): (i) Image enhancement-methods, (ii) 
multitemporal analysis, and (iii) post classification comparison. 
Other approaches combine several methods or consist of novel 
methodologies (an overview can be found in Lu et al. (2003)). 
Image enhancement methods are based on unclassified image 
data which combine the data mathematically to enhance the 
image quality. Examples of these are image difference, image 
ratio, or principal component (PC) and regression analysis. 
Multitemporal analysis methods are based on an isochronic 
analysis of multitemporal image data. This means that n bands 
of an image taken on date T1 and n bands of an image of the 
same area taken on date T2 are merged to form a multitemporal 
image with 2n bands. This merged image is then used to extract 
the changed areas (Khorram et al. 1999). Post classification 
change analysis is based on a comparison of two independently 
generated classification results for at least two dates Tl und T2. 
In addition to simple change detection, this method also 
provides a change analysis; ie., to determine the kind of 
change. It is, however, very sensitive to the achieved 
classification accuracy. 
     
The large number of publications that deal with automated or 
semi-automated change detection prove that this field is an 
important research topic. Prakash & Gupta (1998), for example, 
combine an image difference approach with vegetation indices. 
Lu et al. (2003) merge image difference with a principal 
component analysis. Dai & Khorram (1999) use neural 
networks, whereas Foody (2001) and Nemmour & Chibani 
(2006) involve fuzzy-set theory for change detection. Other 
approaches are based on object-based image analysis (Im et al. 
2008). In summary, a wide range of different methods have 
been developed. These methods have a different grade of 
flexibility, robustness, practicability, and significance. Most 
authors, however, agree that there exist no single best algorithm 
for change detection. Therefore, new methods are still being 
developed and/or adapted especially for the detection of 
damaged buildings and infrastructure in conflict or crisis areas. 
This paper is no exception to this, as it described the 
development of, and the results for, a set of new change 
detection algorithms. They were tested with very high 
resolution (VHR) satellite images of the Dafur conflict area in 
Sudan. Multitemporal images of the affected regions were 
recorded by Quickbird-2 and are displayed on a web site that is 
hosted by Amnesty International clearly showing the 
destruction for a number of villages 
(http://www.eyesondarfur.org/villages.html). With the 
permission of the satellite company Digital Globe, we were able 
to use these preprocessed georeferenced Quickbird data that