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

Chapter

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

were identified through a field study in the time of satel lite snap shoting. This study and maps formed the ground truth data base for our field definition and class identifica tion. The digital computations were performed using inter active image analysis system developed by the author. The mean vector and covariance matrix of each training field were calculated to develop the spectral signature represen tative of land-cover classes. The objective of the analysis, was to discriminate among following 15 agricultural classes: water, red clover, white clover, wood, wheat, maize, mix ture, millet, grass, harvested rapeseed, rapessed, sugarbeet, wheat II, clover and residential area. The resubstitution estimations of the probability of correct classification are summarised in the table. performance. The overall performance of contextual clas sifier is better in tested example than the non-context per- point Bayesian one. Similar improvement is seen for all classes to be searched. This result was reached with the smallest possible context window of eight surrounding pix els. The optimal size of window depends on local condi tions ( average field area ) and on the used image resolu tion. Computational time of presented algorithm was in tested example approximately five times longer than for the standard Bayesian classification one. Further work is still needed to optimize developed software to increase clas sification speed. REFERENCES Peterka,V.,19Sl.Bayesian approach to system identification. In. Trends and Progress in System Identification. Ed. P. Eykhoff , Pergamon Press, Oxford. Tubbs,J.D.,1980. Effect of Autocorrelated Observation on Confidence Sets Based upon Chi-Square Statistics. IEEE Trans. Syst. Man. and Cybern., vol. SMC'-10(4): 177-180. class Contextual Bayesian c. Bayesian classifier 1 1 1 2 0.8 0.72 3 0.9 0.82 4 0.95 0.S1 5 0.98 0.93 6 1 1 7 0.92 0.79 8 1 0.93 9 0.77 0.56 10 0.97 0.97 11 1 0.89 12 0.99 0.91 13 0.85 0.6 14 0.92 0.88 15 0.95 0.68 P 0.93 0.85 We have chosen the autoregressive model of order one (N = 1) and the smallest possible thematic map window of nine pixels (n = 9). In such a case, the contextual classifier needs h(*) — 2010, h( + ) = 1479 operations, while the conventional approach only /?.(*) = 525, h{ + ) = 435 ones. 6 CONCLUSION The tested example shows improvement in classification 342

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