Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

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Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

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

Editor:: Damen, M. C. J.

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:: 1 Visible and infrared data. Chairman: F. Quiel, Liaison: N J. Mulder

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Image optimization versus classification - An application oriented comparison of different methods by use of Thematic Mapper data. Hermann Kaufmann & Berthold Pfeiffer

Document type:: Multivolume work

Structure type:: Chapter

ipectral differences • Saturation increase tufmann 1985). lputing and stretch ssion via photowrite i offers optimum structural mapping ; optimization pro- :ed image product, I procedure the evaluation results e computer. Most of re working pixel- formation. The un pixels with similar ar example with the re space as a cri- The individual class whose center smaller than a vised methods accu- calculate the ses before classi- II pixels to one be carried out using The well known example, is based ihood with which rs of these classes, ed to the class with AVIS, 1978). To get criptions of the n values in each ix, are needed, tistics training ntroduced. The main dures is the se- e sample areas for ds of classification ;upervised pro- ir the classi- escribe the ,s. A multispectral latures for example, ;tural features as le textural parame- ?ach pixel in a .. Well known textu- itistical evaluation ?n neighouring pixels , 1973). The calcula- jmeters for each of jds to 7 additional luent supervised ion, with both the jres, a highly so- er channel combina- out. Furthermore, arocedure (WU, 1975) jll information con- jral channels in a lassification of the euclidean distance ing channels 3/4. ce (d=20 grey values) 11 be discriminated, more spectral classes tic Mapper data te 7.7.1984) a small ms to be a favourable . Fig. 4 demonstrates eads to more than 10

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