Systems for data processing, anaylsis and representation

Allam, Mosaad; Plunkett, Gordon

Bibliographic data

Monograph

Persistent identifier:: 1067490280

Title:: Systems for data processing, anaylsis and representation

Sub title:: ISPRS Commission II Symposium : June 6 - 10, Ottawa, Canada

Scope:: 1 Online-Ressource (XX, 530 Seiten)

Year of publication:: 1994

Place of publication:: Ottawa

Publisher of the original:: The Surveys, Mapping and Remote Sensing, Natural Resources Canada

Identifier (digital):: 1067490280

Illustration:: Illustrationen

Signature of the source:: ZS 312(30,2)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

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

Editor:: Allam, Mosaad; Plunkett, Gordon

Corporations:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Adapter:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Founder of work:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Other corporate:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

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:: Monograph

Collection:: Earth sciences

Chapter

Title:: [Wednesday, June 8, 1994]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: [Poster Session 2-A]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: REFERENTIAL CLASSIFICATION - AN INTELLIGENCE BASED ALGORITHM Othman Alhusain

Document type:: Monograph

Structure type:: Chapter

REFERENTIAL CLASSIFICATION - AN INTELLIGENCE BASED ALGORITHM Othman Alhusain Research Fellow, Department of Photogrammetry, Technical University of Budapest Muegyetem rkp.3.1.24, H-1111 Budapest, Hungary ISPRS Commission II, Working Group 2. KEY WORDS: Image, Classification, Intelligence, Algorithm, ABSTRACT: This paper describes a recently developed method which can be used in the classification of pixels in satellite images. The new method named "Referential Classification" is a concept in which the spectral contents of the pixels are not considered absolutely; but instead they are correlated with a kind of ancillary data that involves the coordinations of a corresponding pixels and their possible variations of spectral data base. The referential classification process is carried out in two subsequent stages. The Application of this method in classifying remotely-sensed images resulted in higher accuracy and less demand for the user intervention during the classification course which imply higher degree of automating the classification process without sacrificing its accuracy. KURZFASSUNG: Im Artikel es geht um die neueste Methode, was im Satellitenbildklassifikation anwendbar ist. Die als "Referential Classification" genannte Methode ist ein Konzept, bei welchem der spektrale Inhalt der Pixel nicht absolutweise berücksichtigt wird, sondern statt dessen er mit verschiedenen Daten korreliert ist, die Koordinaten der entsprechenden Pixel und ihre Variationen des spektralen Datenbasis enthält. Der Referential Classification-Prozeß wird in zwei darauffolgenden Schritten durchgeführt. Die Anwendung der Methode in Klassifizierung von Fernerkundungsdaten ergibt höhere Genauigkeit und braucht wenigeren Eingriff während der Klassifizierung, was ohne Genauigkeitsverlust eine höhere Automatisationsstufe des Klassifikationsprozeßes bedeutet. 1. INTRODUCTION Second, is the ability of the human beings to draw and assemble data making considerable use of previous Classification of remotely sensed images collected by training and experience to make critical decisions (Hunt, multispectral scanners onboard different kinds of 1984). satellites has been performed by conventional methods Referential classification is a concept in which the depending mainly on the spectral content of the pixels spectral contents of the pixels are not considered while ignoring other kinds of information related to the absolutely; but instead they are correlated with the pixels, depending only on one factor which is the coordinations of the corresponding pixels and their spectral content of the pixel and discarding the role of possible variations of spectral database so enhancing the other information about the pixel is a serious pitfall in probability of having correct classification of image the classification procedure. However, there is a clear pixels (Alhusain, 1992). evidence that if one like to have an efficient classification then the classification concept must simulate the human being high ability of perception and 2. REFERENTIAL CLASSIFICATION analyzing (Baxes, 1985). The role of the human beings in the interpretation of The algorithm implemented within this method first image data is of unique and great value for two reasons: proceeds towards assigning Cartesian coordinates (x) First, is the human being's ability to efficiently acquire to each pixel, figure 1 shows the flowchart of the and maintain an awareness of a universal nature; an referential classification method. This might be done awareness not only of the complete remote sensing data locally where the coordinates of each pixel are valid but also of the associated reference data and the only within the image where the assignation process has relationship between the reference and spectral data. been defined, or globally where the coordinates of each 332 pixel are coordinat: all previc about eac image. TI design is : about pix change in on pixels but also assignatio of the imz an image PIX Rep reje Li | N tn O HS Figure -

Access restriction

Copyright

fullscreen: Systems for data processing, anaylsis and representation

Monograph

Chapter

Chapter

Chapter

Table of contents

Cite and reuse

Monograph

Chapter

Image

Image fragment

Citation links

Citation recommendation