XXII ISPRS Congress 2012: Technical Commission VII

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/5: METHODS FOR CHANGE DETECTION AND PROCESS MODELLING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: KERNEL-COMPOSITION FOR CHANGE DETECTION IN MEDIUM RESOLUTION REMOTE SENSING DATA Andreas Ch. Braun, Uwe Weidner, Stefan Hinz

Document type:: Multivolume work

Structure type:: Chapter

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.

Access restriction

Copyright

fullscreen: Technical Commission VII (B7)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation