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/6: REMOTE SENSING DATA FUSION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: INTEGRATED FUSION METHOD FOR MULTIPLE TEMPORAL-SPATIAL-SPECTRAL IMAGES Huanfeng Shen

Document type:: Multivolume work

Structure type:: Chapter

th/ma09 mporal ym SPIE 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 INTEGRATED FUSION METHOD FOR MULTIPLE TEMPORAL-SPATIAL-SPECTRAL IMAGES Huanfeng Shen School of Resource and Environmental Science, Wuhan University, P.R. China shenhf@whu.edu.cn KEY WORDS: Data fusion, remote sensing, multiple temporal-spatial-spectral images ABSTRACT: Data fusion techniques have been widely resear ched and applied in remote sensing field. In this paper, an integrated fusion method for remotely sensed images is presented. Differently from the existed methods, the proposed method has the performance to integrate the complementary information in multip in one unified framework, two general image observati le temporal-spatial-spectral images. In order to represent and process the images ion models are firstly presented, and then the maximum a posteriori (MAP) framework is used to set up the fusion model. The gradient descent method is employed to solve the fused image. The efficacy of the proposed method is validated using simulated images. 1. INTRODUCTION In order to get more information, image fusion techniques are often used to integrate the complementary information among different remote sensing images. By far, a great number of fusion methods for remote sensing images have been developed (Luo et al., 2002; Pohl and van Genderen, 1998). Classical remote — sensing image fusion techniques include panchromatic(PAN) / multi-spectral(MS) fusion (Joshi and Jalobeanu, 2010; Li and Leung, 2009), MS / hyper-spectral(HS) fusion (Eismann and Hardie, 2005) and multi-temporal (MT) fusion (Shen et al., 2009) etc. However, most fusion methods were developed to fuse images from two sensors, and little work attempted to solve the fuse problem of more sensors. In this paper, we propose an integrated fusion method for multiple temporal-spatial-spectral scales of remote sensing images. This method is based on the maximum a posteriori (MAP) framework, which has the performance to fuse images from arbitrary number of optical sensors. 2. IMAGE OBSERVATION MODELS The image observation models relate the desired image to the observed images. Let x —[xj,x5..... Xp, 1 denote the desired image with B, being the total band number. Generally, the band numbers of the observed images are less than or equal to B,. Here we use y to denote the images whose band number is equal to B, and use z to denote the images whose band number is less than B, . Thus, the bth band of the kth image of y can be denoted as yy 5, and the bth band of the kth image of z canbe denoted as z; p - The observation model in terms of y; ; is represented as Jk,p 7 Dy kM y ky k,pXp * Phy kb (1) where S represents the blur matrix, M, , is the motion ykp matrix, D, , is down-sampling matrix, and mn, , represents the noise vector. For convenience, equation (1) can be rewritten as (2) by substituting the product of matrices Sy kb» My and D, with Ay kb Yk,b 7 Ay kb Xp + My Kb (2) The second image observation model relates the desired image x to the observed image z . Generally the band of z is wider than that of x . It has been proved that a wide-band image is almost a linear combination of several narrow-band images when the wide band approximately covers the narrow bands (Boggione et al., 2003; Li and Leung, 2009; Vega et al., 2009). Thus, if the spatial resolutions of x and z are same, the spectral combination model can be denoted as B, za.) — Y epa pp) tia tma) 0) p=l where c, y, is the corresponding weight of the pth band value xp, J) , Tgp 18 aN offset, and n, (i,j) is the noise. It can be expressed in matrix vector form as Up = Cz,k,0X + Tp,pl + M2, kb (4) In more general case, the model can be rewritten as 2.5 7 D; Mz Sz (Cox ou D + Az Ep © Simplifying this equation by multiplying corresponding matrices and vectors Zp = Az i pX + Tok bBo kb TM kb (6) 3. THE FUSION METHOD The proposed method is based on the maximum a posteriori (MAP) framework. For the MAP model, given the images y and z , the desired image can be estimated as: x - argmax p(x | ,2) (7) x Applying Bayes' rule, equation (7) becomes: de ME aX p(x)ply,z| x) (8) x p(y.z)

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