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

2eferential supervised Difference cies. The number of the p(x/w,) will be equal to the number of the ground cover classes. This means, for a pixel at aposition x in multispectral space a set of probabilities can be computed for each class. The required p(w/Xx) of the class and the available p(x/w) of training data are related by the Bay's Theorem as follows: p(wy/x) = p(x/iw) p(w)! p(x) where p(wj is the probability that class w; occurs in the image. The rule in classifying a pixel at a position x will be: x 5 wif pew) pw) > p/w) pw) forall j zi As we mentioned above this method is carried out in two stages, Figure 2, in the first stage and as a result to applying the maximum likelihood classification mentioned above, an index related to the measured spectral content is assigned to each pixel. Then in the second stage each pixel at a time is taken along with its coordinate and the spectral index which was derived in the first stage, then this index is led to the pixel data base and checked against the indices of a pixel with the same coordinates there. If the check result is true this means that the index derived in the first stage is the real spectral content of the classified pixel and classification decision will be confirmed. On the other hand if the check result is false this points to conflicting pixels and means one of two things: the characters of the conflicting pixel have been changed after constructing the data base, or the criteria established for the classification in the first stage is not as accurate as required. In both cases the final decision on classifying such pixels is given to the user where he can adopt the classification results though they are not in line with the information from the data base or he can change the classification criteria. The result of applying this method on classifying a test site image resulted in a good improvement in the image quality and its general appearance. Comparison of the accuracy results between the ordinary and referential classification (table 1) shows that there is 0%, 7%, 13%, 16% and 25% when classifying poplar, water, chestnut, forest, grass, water bodies and sugar beet respectively, and the average accuracy in classifying all the studied classes has been improved by 17.2%. Further comparison between the accuracies of both classification procedures in figure 3 shows that the accuracy of the referential classification is always higher than that of ordinary classification (supervised), the worst case of the referential classification accuracy happens when the data base does not contain any pre- collected information about pixels required to be classified, even in this worst case the referential classification is just as accurate as ordinary classification and is never less as in the poplar class case. 3. CONCLUSIONS Referential classification of image data is a vital step toward automating the classification process which is an important step in automating the whole image processing and analysis process. This can be achieved by cancelling the role of the user in classifying the conflicting pixels where the spectral data allocated to them in the ordinary classification stage can be adopted, changing their classification to ordinary and not referential or they can be rejected and reported as unknown pixels; a multifold process which involves three steps that reflect high level of expert and artificial intelligent behaviour. Reporting about rejected and unknown pixels could be a highly advantageous feature of this method especially when using multitemporal images in constructing the data base about one area and using another image of later time about the same area in the referential classification. Then all rejected pixels in the referential classification could denote possible change in the area between the time of constructing the data base and the present date of classification. However, the referential classification method has slightly disadvantageous features such as the fact that constructing a really indicative data base about individual pixels is not an easy task, though not impossible; also the algorithm for performing this method is more complicated and needs more time and more efficient hardware to be executed. REFERENCES Alhusain, O., 1992. Studies in the Design and Implementation of Microcomputer-based Satellite image Processing System. Ph.D. Thesis, Technical University of Budapest, Budapest, Hungary. Baxes, G.A., 1985. Vision and the Computer- An Overview. Robotics Age, March, pp. 12-19. Hunt, E., 1984. Digital Image Processing- Overview and Areas of Applications. Siemens Forsch.- u. Entwickl. - Ber. Bd. 12, pp. 250-257. Richards, J.A., 1986. Remote Sensing Digital Image Analysis. Springer Verlag, p. 281. Berlin [etc.], Germany. Swain, P.H., 1978. Fundamentals of Pattern Recognition in Remote Sensing. In: Swain and Davis (eds.). Remote Sensing: The Quantitative Approach, pp.136-187. McGraw-Hill, NY, USA. 335

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