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Interpreting and integrating landsat remote sensing image and geographic information system by fuzzy unsupervised clustering algorithm

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Τίτλος: Interpreting and integrating landsat remote sensing image and geographic information system by fuzzy unsupervised clustering algorithm
Συγγραφείς: Chen, Gwotsong Philip
Πηγή: Theses
Στοιχεία εκδότη: Digital Commons @ NJIT
Έτος έκδοσης: 1992
Συλλογή: Digital Commons @ New Jersey Institute of Technology (NJIT)
Θεματικοί όροι: Geographic information systems -- Remote sensing -- Data processing, Earth sciences -- Remote sensing -- Data processing, Landsat satellites, Fuzzy algorithms, Cluster analysis -- Computer programs, Image processing, Computer Sciences
Περιγραφή: Due to the resolution of Landsat images and the multiplicity of the terrain, it is improper to assign each pixel in an image to one of a number of land cover types by using the conventional remote sensing classification method. This is also known as the hard partition method. The concept of the fuzzy set provides the means to resolve this problem. This paper presents a two-pass-mode fuzzy unsupervised clustering algorithm. In the first passing, the cluster mean vectors which represent the geographic attributes or the land cover types are derived. In the second passing, the concept of fuzzy set is used. The cluster mean vectors which are obtained in the first passing are used to derive the membership function. The grade of memberships of each pixel to the land cover types are obtained according to the distance from the pixel to each cluster mean vector. The output of this algorithm can be used as the input of the Geographic Information System.
Τύπος εγγράφου: text
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Γλώσσα: unknown
Relation: https://digitalcommons.njit.edu/theses/2235; https://digitalcommons.njit.edu/context/theses/article/3249/viewcontent/njit_etd1992_060.pdf
Διαθεσιμότητα: https://digitalcommons.njit.edu/theses/2235
https://digitalcommons.njit.edu/context/theses/article/3249/viewcontent/njit_etd1992_060.pdf
Αριθμός Καταχώρησης: edsbas.EFC487F5
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  Data: Interpreting and integrating landsat remote sensing image and geographic information system by fuzzy unsupervised clustering algorithm
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Gwotsong+Philip%22">Chen, Gwotsong Philip</searchLink>
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  Data: Theses
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  Data: Digital Commons @ NJIT
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  Data: 1992
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  Data: Digital Commons @ New Jersey Institute of Technology (NJIT)
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  Data: <searchLink fieldCode="DE" term="%22Geographic+information+systems+--+Remote+sensing+--+Data+processing%22">Geographic information systems -- Remote sensing -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Earth+sciences+--+Remote+sensing+--+Data+processing%22">Earth sciences -- Remote sensing -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Landsat+satellites%22">Landsat satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+algorithms%22">Fuzzy algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+--+Computer+programs%22">Cluster analysis -- Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Sciences%22">Computer Sciences</searchLink>
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  Data: Due to the resolution of Landsat images and the multiplicity of the terrain, it is improper to assign each pixel in an image to one of a number of land cover types by using the conventional remote sensing classification method. This is also known as the hard partition method. The concept of the fuzzy set provides the means to resolve this problem. This paper presents a two-pass-mode fuzzy unsupervised clustering algorithm. In the first passing, the cluster mean vectors which represent the geographic attributes or the land cover types are derived. In the second passing, the concept of fuzzy set is used. The cluster mean vectors which are obtained in the first passing are used to derive the membership function. The grade of memberships of each pixel to the land cover types are obtained according to the distance from the pixel to each cluster mean vector. The output of this algorithm can be used as the input of the Geographic Information System.
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      – SubjectFull: Geographic information systems -- Remote sensing -- Data processing
        Type: general
      – SubjectFull: Earth sciences -- Remote sensing -- Data processing
        Type: general
      – SubjectFull: Landsat satellites
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      – SubjectFull: Fuzzy algorithms
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      – SubjectFull: Cluster analysis -- Computer programs
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      – SubjectFull: Image processing
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      – SubjectFull: Computer Sciences
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              Y: 1992
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