XXII ISPRS Congress 2012: Technical Commission VII

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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/4: METHODS FOR LAND COVER CLASSIFICATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: LAND COVER CLASSIFICATION OF MULTI-SENSOR IMAGES BY DECISION FUSION USING WEIGHTS OF EVIDENCE MODEL Peijun Li and Bengin Song

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 LAND COVER CLASSIFICATION OF MULTI-SENSOR IMAGES BY DECISION FUSION USING WEIGHTS OF EVIDENCE MODEL Peijun Li * and Bengin Song Institute of Remote Sensing and GIS, Peking University, Beijing 100871, P R China Emails: pjli@pku.edu.cn, sbq43031@126.com Commission VIL, WG VII/4 KEY WORDS: weights of evidence, multi-sensor, land cover classification, decision fusion ABSTRACT: This paper proposed a novel method of decision fusion based on weights of evidence model (WOE). The probability rules from classification results from each separate dataset were fused using WOE to produce the posterior probability for each class. The final classification was obtained by maximum probability. The proposed method was evaluated in land cover classification using two examples. The results showed that the proposed method effectively combined multisensor data in land cover classification and obtained higher classification accuracy than the use of single source data. The weights of evidence model provides an effective decision fusion method for improved land cover classification using multi-sensor data. 1. INTRODUCTION With the increasing availability of digital images acquired by different sensors, it is crucially important to effectively process and analyze these multisensor data sets in diverse applications, since these data provide complementary information for improved land cover mapping and monitoring. The development of new and effective image fusion methods has been one of the important topics in remote sensing information processing and applications. This study proposes a novel decision fusion method based on the weights of evidence model (Good, 1985; Spiegelhalter and Knill-Jones, 1983) to combine multi-sensor data for improved land cover classification. 2. METHODS The proposed method can be summarized as follows. The land cover classification was first separately conducted on each dataset using a supervised classifier. The obtained classification results from different datasets were then fused using the weights of evidence model. 2.1 Weight of evidence model The weights of evidence model is a data integration method. The model was originally developed for medical diagnosis based on presence or absence of a set of symptoms (Good, 1985; Spiegelhalter and Knill-Jones, 1983), and was subsequently adopted for mineral potential mapping (Bonham-Carter et al., 1988, 1989). The weights of evidence method used in this study is derived from those used in medical diagnostics. Suppose that there are E.i=12,..0 multiple source of evidence. Let , be the ith binary evidence which is related to the occurrence of a class, i.e. * Corresponding author. an event D. E and E denote the presence and absence of the evidence, respectively. The probability of the occurrence of a class in a location (pixel), given the presence and absence of the evidence E; can be given by the following conditional probabilities: P(DNE;) P(E, | D) PAR EN ( EY gj. PonE) | 5E 1D) A I EE], i (D These two equations can be also expressed in terms of the odd: (DE) - o0). olpiE)- otn : (2) Where O(D |E ;) and O(D | E 3 are ratio of the posterior probabilities of D given the absence and presence of the evidence E. : The weights for evidence E ; in a WOE model are calculated ran £1») P(E,|D) i Fl? |p) ; P(E | D) "

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