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

- verification of the tr aining sets - it is necessary to verify the labeling of training polygons and to test the separability of target classes - editing of the training samples using k - k x nearest neighbour rule - suggestion of appropriate spectral relations to be used for discrimination of target classes - knowledge base development (or updating) - the results of various tests are saved in the data base - generation of the classification algorithm - classification of the image data. Of course, classification method described has also some limitations: - the pixels have to be well illuminated - pixel with anomalous illumination (dense shadows) cannot be recog nized correctly - the pixels have to be relatively "pure" , the spectral properties are influenced by a single target class - the target classes can be recognized using the spectral properties only - the spatial and textural properties are not used. Verification of the training samples The collection of a suitable training samples and the decision in which classes may by classified the satellite image data create the serious problem. During the collection of training sets some of the training polygons are assigned to a certain class. It is necessary to verify, whether these polygons really belong to the same target class. Some methods solving the problem of unperfect labeling of training polygons (for normally distributed data) have been already investigated [ 2 ]. The decisions are made by comparison of mean values and covariance matrices. If we do not dispose with normal data distribution, then it is possible to use a method applying mutual information [ 3 ], [ 7 ]. Editing of training samples It has been shown, that the editing of training set improves the performance of the classifier. The k - k x nearest neighbour method is relatively simple. The k nearest neighbours from the whole training set are found for every sample. The tested sample is edited from the training set, when not being classified in accordance with its true class membership (when at least k x of its nearest neighbours do not belong to the same class as the tested sample). The suggestion of spectra1 relations The spectral relations characterize the shape of spectral reflectance curves in terms of certain inequalities to avoid the use of absolute values of individual features. The analyst can suggest an arbitrary spectral index using his empirical experience, studies of the literature or studies of spectral reflectance curves of target classes. The analyst suggests spectral indices, which seems to be typical for individual classes. Of course, the set of spectral indices from previous

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