Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

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Title:: [WP-1 ADVANCED COMPUTING FOR INTERPRETATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: CONTEXTUAL BAYESIAN CLASSIFIER. Michal Haindl

Document type:: Multivolume work

Structure type:: Chapter

340 CONTEXTUAL BAYESIAN CLASSIFIER Miclial Haindl Institute of Information Theory and Automation Czechoslovak Academy of Sciences Pod vodarenskou vezi 4, 182 08 Prague 8 Czechoslovakia ISPRS Commission VII Abstract A new type of Bayesian classifier is introduced to recognize remote sensing image data. The classifier uses contextual information about the classified pixel surrounding based on autoregression model prediction. Key Words: Bayesian Classifier 1 INTRODUCTION The conventional approach to remote sensing picture data classification is to perform test on unknown pixel against all classes using a spectral feature subset and then assign the unknown pixel to one of these classes, not taking re specting the large spatial correlation (Tubbs,2). The inter pretation of picture element - pixel does not depend upon any relationship with any other pixel, so we do not use all available information. Not using context has fundamental limitation influence on classification accuracy for machine recognition. On the other hand the use of all context in formation is paid in unsolvable increase of computational demands. Possible solution is a compromise. The developed decision rule is the optimal contextual Bayesian classifier with several simplifications. We assume local neighbour pixels to be conditionaly statisticaly inde pendent, their class conditional density to be independent on neighbour labels and we neglect the space arrangement of local neighbour labels. In the first step the thematic map as the output from per-point Bayesian classifier is created. The second step consists of Bayesian classifier in which formula the apriori class probabilities are replaced by non- causal frequency predictor. This predictor is based on the autoregressive model of class frequences estimated from the first step classification. 2 CONTEXTUAL CLASSIFIER Let us chose some direction of movement on the image plane, for example row scanning from left to right and top to bottom. According to this choice the following index is used throughout this paper: t = (i-l)N , + j (1) where i,j = 1,2,..., N' is row and column index, respec tively. N' x !\ rl is the size of classified image. Let us de note X t (multidimensional pixel) in time t of scanning. u>, i = 1,..., A class indicator,Y t the A—dimensional vector, which i-th compound is the occurence frequency of u t in a window D t .D t window is defined as the set of thematic map entries within a given region centered around the class indicator corresponding X t . The window is defined to be perpendicular with odd number of class indicators in both directions, so that each frequency vector Y can be assigned to the center pixel of its respective window. Let us denote the set of past thematic map windows D (t) = {£> t , A-i, • • •, Di) (2) and Lflb the set of all pixel indexes from D. We have chosen the noncausal frequency predictor: Y t = E[Y t \D (t ~ l) ] (3) Let us denote Mi(Xt) = (Xt - - ta) (4) where /q, E, are mean value vectors and covariance ma trices (in practise their estimators) of single classes. Now, the modified Bayesian decision rule using the context in formation abont the class membership of surrounding, can be put in the form: Assign feature vector X t into class uq if Mi(X t ) + In |£i| - 2 In Y t (i) = min {A/ J (A'«) + ln|E J |-21nV;(i)} (5) The thematic map for prediction construction is as sumed to be the output from per-point Bayesian classifier . then the relation (3) can be put in the form (6) Y t = E[Y t | min {Mj(X m ) + In |Ej| - 2 In Pj} : m e £) (t-1) ] (6) where P 3 are some prior class probabilities estimations.

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