Full text: Technical Commission VII (B7)

    
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 
    
KERNEL-COMPOSITION FOR CHANGE DETECTION IN MEDIUM RESOLUTION 
REMOTE SENSING DATA 
Andreas Ch. Braun, Uwe Weidner, Stefan Hinz 
Institute of Photogrammetry and Remote Sensing 
KIT - Karlsruhe Institute of Technology 
Englerstr. 7, 76131 Karlsruhe 
andreas.ch.braun @kit.edu, uwe.weidner @kit.edu, stefan.hinz@kit.edu 
http://www.ipf kit.edu 
Commission VII/5 
KEY WORDS: Change Detection, Classification, Landuse, multispectral, multitemporal, Landsat 
ABSTRACT: 
A framework for multitemporal change detection based on kernel-composition is applied to a multispectral-multitemporal classification 
scenario, evaluated and compared to traditional change detection approaches. The framework makes use of the fact that images of 
different points in time can be used as input data sources for kernel-composition - a data fusion approach typically used with kernel 
based classifiers like support vector machines (SVM). The framework is used to analyze the growth of a limestone pit in the Upper 
Rhine Graben (West Germany). Results indicate that the highest accuracy rates are produced by the kernel based framework. The 
approach produces the least number of false positives and gives the most convincing overall impression. 
1 INTRODUCTION 
Although the availability of modern remote sensing datasets in- 
creases — e.g. hyperspectral, high resolution optical satellite im- 
agery, interferometric synthetic aperture radar — many change de- 
tection applications continue to require traditional datasets. The 
fact that e.g. Landsat data are available since 1972 makes them 
a valuable source of information for four entire decades. They 
can be seen as a way to recover information on past environ- 
mental conditions which are not observable any more by field 
campaigns. However, traditional methods like post-classification 
change detection based on overlaying classification maps raise 
accuracy issues (Serra et al., 2003). Therefore, more sophisti- 
cated change detection methods have been proposed in literature. 
Some approaches model the probability of transition from one 
class to another. Another approach is change detection based 
on kernel-composition (Camps- Valls et al., 2006b). These issues 
are exemplified on a change detection application from Upper 
Rhine Graben. Changes in the landuse are outlined with spe- 
cial focus on the construction of a limestone pit which contin- 
uously grows replacing near-natural ecosystems. Kernel based 
classifiers — like the well know support vector machine (SVM) 
(Boser et al., 1992), (Cortes and Vapnik, 1995) — work on kernel 
matrices. These kernel matrices represent the similarity between 
data points in high dimensional feature spaces (reproducing ker- 
nel Hilbert spaces, RKHS). The SVM chooses the most suitable 
points by optimizing a target function on the kernel choosing only 
a few training data points as SVs. These points are used to de- 
fine a separating hyperplane which is usually non-linear in the 
input space. The conceptual advantage of kernel-composition 
is, that different kernel functions can by combined e.g. by ad- 
dition, thus performing data fusion in the RKHS during classi- 
fication. This circumstance is usually employed for data fusion 
(Tuia and Camps-Valls, 2009). However, it can also be employed 
for change detection. In (Camps- Valls et al., 2008), (Camps- Valls 
et al., 2006a) well know change detection techniques — like im- 
age differencing — are adapted for kernel-composition based ap- 
proaches. An entire framework for change detection and mul- 
titemporal classification is presented. This framework is evalu- 
  
Figure 1: Limestone pit near Heidelberg 
ated and compared against traditional approaches herein. We ex- 
emplify this framework analyzing the growth of a limestone pit 
near the city of Heidelberg between 2001 and 2005. The kernel 
based approaches are compared against traditional approaches 
like post-classification change detection and an approach based 
on stacking features before classification. Although traditional 
approaches provide valuable information on the overall growth 
of the limestone pit, kernel based approaches provide more ro- 
bust results which are visually more convincing and quantita- 
tively more accurate. The remainder of the paper is organized 
as follows. Sec.2 provides the mathematical foundations of the 
methods used. Sec.3 gives an overview of relevant literature in 
change detection and kernel-composition methods. The results 
of the comparison are presented in Sec.4 and discussed in Sec.5. 
Finally, Sec.6 concludes the contribution.
	        
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