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/4: METHODS FOR LAND COVER CLASSIFICATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: CLASSIFICATION OF ACTIVE MICROWAVE AND PASSIVE OPTICAL DATA BASED ON BAYESIAN THEORY AND MRF F. Yu, H. T. Li, Y. S. Han, H. Y. Gu

Document type:: Multivolume work

Structure type:: Chapter

Wy = arg max{p(w| X)} = arg max{p(X |w)p(w)} (0 ^ where W, = MAP (Maximum a Posteriori) estimation of "Map the field of class labels which maximizes the posterior cost function (1). p(w)= prior probability distribution p(X]w)- class-conditional distribution Therefore, the modeling of both the p(w) and p(X|w) becomes an essential task. 2.1. Prior Distribution Model-MRF The introduction of MRF can be found in many texts (Chellappa, 1983; 1985). The image function w(s) can be taken a two-dimensional random, and expressed by Markov random field as: p(s) [WS —5)} = p{(w(s) | w(0s)} (2) where S = image lattice Os = neighborhood system So for a given point in a two-dimensional random, its class label is only dependent on its neighbors and unrelated with other pixels of image. For a given neighborhood system, a Gibbs distribution is defined as any distribution p(w) that can be expressed in (Julian, 1986) as: 1 1 c p(w) = at V (w)] (3) where J(w)- arbitrary function of w on the clique c, C - the set of all cliques z = normalizing constant called a partition coefficient 7 = analogous to temperature. The prior distribution based on the first order neighborhood system as: plu) = i | exp[-— Y. V:Qwyj--l-expl-BY.r(Q] — (5 ceC z ceC 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 where pf = weight emphasizing the significance of interactions among adjacent pixels inside the clique, f(w) = V'(w) mathematically. So (1) can be further written as: Wig = AIG min 5 (- In p(X |w(s)* B» .tt()] 6 ses ceC where w(s) = class label at s € S. 22. Modeling the Conditional Probability Density Function As the impact of speckle noise of SAR image in synthesizing classification of the optical and microwave images, it is difficult to obtain the conditional probability density function of the multisource remote sensing data, maximum likelihood classifier with modified M-estimates of mean and covariance (MMLM) can be used to classify the multisource images and get the initial class labels and the conditional probability density function of each class. From the Reference (Yonhong, 1996), we see that MLMM can obtain a good precision of classification and proper conditional probability density function, also restrain the speckle noise of SAR images. 2.3. Classification by Iterated Conditional Modes (ICM) The ICM is computationally feasible since it updates the class assignments iteratively (Julian, 1986), the objective is to estimate the class label of a pixel given the estimates of class labels for all other pixels inside the rectangular lattice. Then the optimization problem of (5) becomes: W(s) = arg max[ p(w(s) | X, W(S — s))] (6) w(s) Applying the Bayes' rule and considering the Markov property (Julian, 1986), the argument of (6) becomes: W(s) = armar (s) | W(s)} p{w(s) | W(@s)}] (7) From the Hammersley-Clifford theory (Geman, 1984) we know: piw(s)| w(05)5 — Zexp t- BU [w(s), W(8s)]) (8) [w(s), M(@s)1= 3 [178,5 55] (9) ceC

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