Actes du Symposium International de la Commission VII de la Société Internationale de Photogrammétrie et Télédétection: Actes du Symposium International de la Commission VII de la Société Internationale de Photogrammétrie et Télédétection

Laidet, L.

Actes du Symposium International de la Commission VII de la Société Internationale de Photogrammétrie et Télédétection (Volume 2)

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Multivolume work

Persistent identifier:: 1669258823

Title:: Actes du Symposium International de la Commission VII de la Société Internationale de Photogrammétrie et Télédétection

Sub title:: Toulouse, 13 - 17 Septembre 1982

Year of publication:: 1982

Place of publication:: Toulouse

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 1669258823

Language:: English

Editor:: Laidet, L.

Corporations:: International Society for Photogrammetry and Remote Sensing, Commission VII, Interpretation of Data

Adapter:: International Society for Photogrammetry and Remote Sensing, Commission VII, Interpretation of Data

Founder of work:: International Society for Photogrammetry and Remote Sensing, Commission VII, Interpretation of Data

Other corporate:: International Society for Photogrammetry and Remote Sensing, Commission VII, Interpretation of Data

Document type:: Multivolume work

Volume

Persistent identifier:: 1669259897

Title:: Actes du Symposium International de la Commission VII de la Société Internationale de Photogrammétrie et Télédétection

Scope:: XV, 415 Seiten

Year of publication:: 1982

Place of publication:: Toulouse

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 1669259897

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(24,7,2)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.

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

Editor:: Laidet, L.

Corporations:: International Society for Photogrammetry and Remote Sensing,, Commission VII, Interpretation of Data

Adapter:: International Society for Photogrammetry and Remote Sensing,, Commission VII, Interpretation of Data

Founder of work:: International Society for Photogrammetry and Remote Sensing,, Commission VII, Interpretation of Data

Other corporate:: International Society for Photogrammetry and Remote Sensing,, Commission VII, Interpretation of Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: WG VII-1 METHODOLOGIE POUR L'AMELIORATION ET LA CLASSIFICATION THEMATIQUE DES DONNEES VISIBLES ET INFRAROUGE. - METHODOLOGY FOR ENHANCEMENT AND THEMATIC CLASSIFICATION OF VISIBLE AND INFRARED DATA.

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: [SESSION 19]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: The Expanded PDCS Digital Image Processing System (EPDCS) by G. H. Xu [...]

Document type:: Multivolume work

Structure type:: Chapter

The boundary transformation from the local coordinate systan to the corresponding line and column number of the landsat subsence, is performed by the programme which will make an affine transforma- tion. 3/ Data from a mask which represents training areas by means of different thematic codes. B. The histogramme and the symbol coded picture of the training area. The programme can display them either on the terminal Screen or on the line-printer. C. When given thresholds, the pixels, with digital levels excee- ding the thresholds, are deleted from the symbolic picture and will not be included in the calculation of the statistics of the training area. D. Calculating statistical values as averages, standard devia- tions, covarience matrices and correlation matrices of the training areas. At last, the programme will give the user the possibility to combine the statistical values of the training arcas extracted from either optional files or the terminal, and to delete some statistical values which the user does not require. : the user can store all the statistical values of the training areas onto a disk file. 5. Supervised Classification Classification is based on various supervised classification techniques that require refrence signatures of targets represented by training areas on the ground. The EPDCS system provides the user with three different Super- vised classificution methods-maximum likelihood classification, minimum distance classification and table look-up classification. A user can select different training areas, channels and thresholds for the classification, using data stored on the same file. The classified image on the paper with a symbolic code representing different classes are produced by line-printer. Classified colour or black-white image on the film will be produced by rotating drum recorder. 6. Un-Supervised classification The supervised technique is associated with high variability of spectral signatures. When so, it is difficult to set up an opera- tional library of refenence signatures. You have to obtain the re- ference signatures directly from training areas. Even in this prac- tice, the training areas selected by the user are not objective. The unsupervised classification method avoids the above mentioned diffi- culty by not requiring the reference signatures in the data proces- ing phase. After the data processing the identification is done by pixelwise testing of the class assignment. The EPDCS system has three programmes for unsupervised classification. The first cluster- ing algorithm is used to group pixels with similar spectral charac- teristics. The second clustering algorithm is used to group pixels by means of the local textural parameter which was proposed by H. BEGUIN, H,Do TU and J. WILMET. The third algorithm also consists in combining spectral information with spatial information. Firstly the image is separated into the "small regions", pixels of which must be neighbours and must have very similar spectral values. Then, the unsupervised classification based on the "small regions" is per- formed according to spectral mean value and deviation of the regions he re vised clas Spectral | having sin ers from % formation is more us assignment processing within an sent diffe 8. Accurao In ord truth, we and map by 7. Ciscanin m "ing paramo Kk * Dif The ma fied, by m which have bols repre B. Con The ta es. C. Ovo D. Map B. Ove ' ACKNOW] I am v Mr, Gôran ; University ideas and

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