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:: LANDUSE CLASSES DISCRIMINATION WITH SATELLITE IMAGES BASED ON SPECTRAL KNOWLEDGE. Vladimir Cervenka , Karel Charvót

Document type:: Multivolume work

Structure type:: Chapter

327 classification can be used. Ihis set has been saved in the spectral knowledge data base. In this case, the values of suitable thresholds T are updated. It is possible to use so called spectral transformations. They include linear combinations of spectral bands or various ratios of bands, e.g. brightness, greeness, yelowness [ 4 ] or PVI (perpendicular vegetation index - [ 5 ]). Many authors have found out that spectral transformations are correlated with vegetation growth parameters (which can correspond to various target classes). Dusek [ 6 j has shown that winter wheat vegetation indices (leaf area index, percent green ground cover) are highly correlated with Thematic Mapper band ratios. For example, the three band ratios, that produce the highest correlation with the leaf area index are: TM4 . TM5 / TM3 TM4 . TM5 / TM1 TM1 . TM4 . TM5 / TM2 . TM3 . The spectral knowledge base development The aim of this operation is to find the appropriate thresholds for spectral indices so that the resulting spectral relations will have a maximal power of discrimination. The spectral knowledge based system proposes a set of possible thresholds for every spectral index in an automatic way. Then, every target class (its training samples respectively) is examined, whether (and up to which degree) fulfil the spectral relation. The results are saved into the data base. This process may be divided into five steps: - calculation of the spectral index for all training samples - the histogram of computed values (for every class) is created - the threshold selection - all thresholds, which separate at least two target classes are selected - the test, which classes fulfil the spectral relation - the result is saved for later evaluation. Seneration of the c lass if ic at i onjl g or i t h m._ The classifier suggested is of binary tree character. A binary tree classifier assigns a class label to a sample by passing it through the tree from root to leaf. Leaves are labeled by class labels. The test of a spectral relation is performed in every node (except the leaves). Here, the features of pixel being classified are considered. If the test is fulfilled, then the test, corresponding to the right son, is performed. Otherwise, the testing continues in the left son. The label of target classes are assigned to a classified pixel at final level of the tree. The binary tree is constructed in an interactive way. The set of target classes is determined for every node (the root corresponds to the set of ail classes). Then, some spectral relations are chosen from the data base which are suitable for the separation of this set of classes. Two subsets S\ S2 of target classes are distinguishable on the basis of a spectral

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