XXII ISPRS Congress 2012: Technical Commission VII

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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:: 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

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 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

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