Yapay Sinir Aǧlari ile Uzaktan Algilama Görüntülerinin Bölütlenmesi

Tamer Ölmez; Zümray Dokur

Yapay Sinir Aǧlari ile Uzaktan Algilama Görüntülerinin Bölütlenmesi

Translated title of the contribution: Segmentation of remote-sensing images by artificial neural networks

Tamer Ölmez^*, Zümray Dokur

^*Corresponding author for this work

Department of Electronics and Communication Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this study, a novel unsupervised incremental neural network is proposed for the segmentation of remote sensing images. Feature vectors are formed by the intensity of one pixel of each channel. The trainning set of DAYS network is formed by using all pixels of the image. The remote sensing image is segmented according to the decision of the network. In the study, the segmentation results of DAYS and Kohonen networks are compared

Translated title of the contribution	Segmentation of remote-sensing images by artificial neural networks
Original language	Turkish
Title of host publication	Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004
Editors	B. Gunsel
Pages	84-86
Number of pages	3
Publication status	Published - 2004
Event	Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004 - Kusadasi, Turkey Duration: 28 Apr 2004 → 30 Apr 2004

Publication series

Name	Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004

Conference

Conference	Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004
Country/Territory	Turkey
City	Kusadasi
Period	28/04/04 → 30/04/04

Cite this

@inproceedings{d254f63e77fd4db5ac4da43cefdedf7c,

title = "Yapay Sinir Aǧlari ile Uzaktan Algilama G{\"o}r{\"u}nt{\"u}lerinin B{\"o}l{\"u}tlenmesi",

abstract = "In this study, a novel unsupervised incremental neural network is proposed for the segmentation of remote sensing images. Feature vectors are formed by the intensity of one pixel of each channel. The trainning set of DAYS network is formed by using all pixels of the image. The remote sensing image is segmented according to the decision of the network. In the study, the segmentation results of DAYS and Kohonen networks are compared",

author = "Tamer {\"O}lmez and Z{\"u}mray Dokur",

year = "2004",

language = "T{\"u}rk{\c c}e",

isbn = "0780383184",

series = "Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004",

pages = "84--86",

editor = "B. Gunsel",

booktitle = "Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004",

note = "Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004 ; Conference date: 28-04-2004 Through 30-04-2004",

}

Ölmez, T & Dokur, Z 2004, Yapay Sinir Aǧlari ile Uzaktan Algilama Görüntülerinin Bölütlenmesi. in B Gunsel (ed.), Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004. Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004, pp. 84-86, Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004, Kusadasi, Turkey, 28/04/04.

Yapay Sinir Aǧlari ile Uzaktan Algilama Görüntülerinin Bölütlenmesi. / Ölmez, Tamer ; Dokur, Zümray.
Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004. ed. / B. Gunsel. 2004. p. 84-86 (Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Yapay Sinir Aǧlari ile Uzaktan Algilama Görüntülerinin Bölütlenmesi

AU - Ölmez, Tamer

AU - Dokur, Zümray

PY - 2004

Y1 - 2004

N2 - In this study, a novel unsupervised incremental neural network is proposed for the segmentation of remote sensing images. Feature vectors are formed by the intensity of one pixel of each channel. The trainning set of DAYS network is formed by using all pixels of the image. The remote sensing image is segmented according to the decision of the network. In the study, the segmentation results of DAYS and Kohonen networks are compared

AB - In this study, a novel unsupervised incremental neural network is proposed for the segmentation of remote sensing images. Feature vectors are formed by the intensity of one pixel of each channel. The trainning set of DAYS network is formed by using all pixels of the image. The remote sensing image is segmented according to the decision of the network. In the study, the segmentation results of DAYS and Kohonen networks are compared

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M3 - Konferans katkısı

AN - SCOPUS:18844453225

SN - 0780383184

SN - 9780780383180

T3 - Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004

SP - 84

EP - 86

BT - Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004

A2 - Gunsel, B.

T2 - Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, SIU 2004

Y2 - 28 April 2004 through 30 April 2004

ER -

Yapay Sinir Aǧlari ile Uzaktan Algilama Görüntülerinin Bölütlenmesi

Abstract

Publication series

Conference

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