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

LANDUSE CLASSES DISCRIMINATION WITH SATELLITE IMAGES 8ASE0 ON SPECTRAL KNOWLEDGE Vladimir Cervenka , Karel Charvbt Geodetic and Cartographic Enterprise, Prague Earth Remote Sensing Centre Introduction At present, muitispectrai in many remote sensing attention has also been automatic interpretation principal approaches to unsupervised one. image data are frequently exploited applications. Therefore, a great paid to the development of their (classification). There are two the classification: supervised and A method of supervised classification of data, based on spectral knowledge, is contribution. Training data are collected f using the supervised classification. The samples has to be representative, but ra creation of sufficiently representative tr a serious problem. Satellite images cover nevertheless it is difficult to find suitabl which cover the whole feature space. muitispectrai image described in this or every class when choice of training ndom. However, the aiming sets may be some hundreds km 2 e training samples, It is necessary to find such classification rules, which generalize the properties of training samples (being localized in a certain part of image only) for the whole area of interest. The approach based on the creation of data base of spectral knowledge seems to be an appropriate solution of the problem mentioned. Such classification system characterizes the target classes in terms of numerical rules, which reflect characterictic relations between spectral bands. The band-to-band relations describe the shape of the spectral reflectance curves [ 1 ]. The spectral knowledge based classification - overview The spectral knowledge based approach prefers the description of target classes on the basis of certain relations between individual spectral features [ 1 ]. This approach can be used to avoid the scene-specific limitations - the data base of spectral knowledge can be used (to a certain extent only) for classification of further scenes. ine target classes are described using the inequalities (so called spectral relations), which can be written in the general form SI > T , where SI is a spectral index (a numerical expression in terms of spectral features) and T is a threshold. Every spectral relation is evaluated in regard to the ability to separate various subsets of target classes. It is necessary to find out, which target classes fulfil a certain spectral relation. This knowledge can be used for target classes discrimination using a binary tree classifier. Ihe whole classification procedure could be divided into following steps: - collection of suitable training samples for every class 325

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