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:: MULTI-TEMPORAL SAR CHANGE DETECTION AND MONITORING S. Hachicha, F. Chaabane

Document type:: Multivolume work

Structure type:: Chapter

gs ce. m sel \R se, nd ric -) de Jy /0- e, Ito 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 MULTI-TEMPORAL SAR CHANGE DETECTION AND MONITORING S. Hachicha, F. Chaabane Carthage University, Sup'Com, COSIM laboratory, Route de Raoued, 3.5 Km, Elghazala Tunisia. ferdaous.chaabene@supcom.rnu.tn KEY WORDS: Spatio-temporal NL-means filtering, Rayleigh Kullback Leibler, Rayleigh Distribution Ratio, DSMT fusion, temporal classification. ABSTRACT: Multitemporal SAR images are a very useful source of information for a large amount of applications, especially for change detection and monitoring. In this paper, a new SAR change detection and monitoring approach is proposed through the analysis of a time series of SAR images covering the same region. The first step of the method is the SAR filtering preprocessing step using an extension of the spatial NL-means filter to the temporal domain. Then, the Rayleigh Kullback Leibler and the Rayleigh Distribution Ratio measures are combined to detect the changes between a reference image and each SAR image of the time series at both local and global scale. These measures are combined using the Dezert-Smarandache theory which takes into account conflicts between sources and thus enhances the dual change detection results. Finally, a pixel based temporal classification is applied starting from the obtained change maps in order to describe the temporal behaviour of the covered regions. 1. INTRODUCTION Remote sensing applications have known a fast expansion thanks to the diversity and the amount of satellite images. This paper focuses on the problem of change detection and monitoring. SAR data change detection allows the analysis of land phenomenon for a large range of applications such as the urban and agriculture regions monitoring, the mapping of damages following a natural disaster, etc. These changes can be of different types, origins and durations. In the literature, several approaches have been proposed for SAR change detection between two images. These methods can be classified in two classes: approaches based on pixel intensity and approaches based on local statistics. The first set of approaches is based on the pixel intensity and depends on the neighbouring pixel intensity inside the analysis window. They include image differencing, log-ratio measures [5], Rayleigh Distribution Ratio (RDR) [3], etc. For the second set of techniques, the local statistics are estimated by considering some reasonable distributions which, in practice, have been found particularly convenient in the case of SAR scenes. Indeed, the first class of measures is limited to the comparison of first order statistics. More information may be extracted from the comparison of the local probability density functions (pdfs).Once the pdfs parameters are estimated, their comparison can also be performed using different criteria and the most usual one is the Kullback-Leibler divergence [4]. All these techniques are designed for comparison between only two SAR images. Not many works were interested to the change detection and monitoring over a time series of SAR images [8]. Most of them are feature and model based Techniques, pixel based clustering techniques or frequent sequential pattern based techniques [6]. In this paper, we are interested in SAR change detection and monitoring through the analysis of a time series of SAR images covering the same region [10]. The aim of this work is to consider the radiometric information and to characterize the change by its temporal signature and evolution. Then the investigation concerns the identification of the changes and their monitoring using a temporal classification. We believe that changes can be categorized in four classes: - Abrupt changes such as urban constructions, deforestation area, etc, - Evolutionary changes such as vegetation areas, - No changes areas i.e. stable areas as urban areas for example, - Periodic changes such as the vegetation evolution according to seasons. Thus, dual SAR change detection indicators are going to be used in order to classify the changes according to their temporal behavior. The main contribution of this work consists in the introduction of a judicious combination of two families SAR change detection indicators in the change monitoring approach which can be considered as an improvement of the work done in [10]. Indeed, the proposed technique is based on a combination between a local statistics and a pixel intensity change detection indicators. It suggests an original strategy for changes temporal classification using the behaviour of the dual change measures between each SAR image and a constructed reference image. The proposed multi-temporal change detection and monitoring approach follows these main steps (cf. figurel): a) As a preprocessing step, we apply a spatio-temporal adaptive filtering of the SAR sequence to reduce the speckle noise and estimate the reflectivity [10]. b) Construction of the reference image which represents an intermediate state throughout the SAR time series. c) Identification of the changes between the generated reference image and each filtered SAR image using a local statistics change detection family operator which assumes a Rayleigh distribution for a non local neighbourhood [10]. The contribution of this paper is to introduce a pixel intensity change detection indicator applied on non filtered images. Thus, the local statistics and a pixel intensity change detection measures are combined using the DSM theory [11] in order to detect temporal changes. Both local and global variations are then detected by the resulted detector.

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