Yiǧilmiş genelleme yöntemiyle uydu görü ntülerinden otomatik bina bulunmasi

Bariş Yüksel; Çaǧlar Şenaras; Mete Özay; Fatoş Yarman Vural

doi:10.1109/SIU.2011.5929746

Yiǧilmiş genelleme yöntemiyle uydu görü ntülerinden otomatik bina bulunmasi

Translated title of the contribution: Automatic building detection from satellite images using stacked generalization architecture

Bariş Yüksel, Çaǧlar Şenaras, Mete Özay, Fatoş Yarman Vural

INF - System Information Sciences

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

Abstract

This paper proposes an automated segmentation, based algorithm for building detection in satellite images. The proposed method consists of a two layer hierarchical classification mechanism. In the first layer, each segment is classified by N different classifier according to different features. In the second layer of the mechanism, the class membership values of the segment from different first layer classifiers are concatenated to form a new vector, which is used by the meta classifier to classify the selected segment. The paper also presents the performance results of the proposed model and comparison with the single layer classifiers.

Translated title of the contribution	Automatic building detection from satellite images using stacked generalization architecture
Original language	Turkish
Title of host publication	2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011
Pages	694-697
Number of pages	4
DOIs	https://doi.org/10.1109/SIU.2011.5929746
Publication status	Published - 2011 Jul 21
Event	2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011 - Antalya, Turkey Duration: 2011 Apr 20 → 2011 Apr 22

Publication series

Name	2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011

Conference

Conference
Country	Turkey
City	Antalya
Period	11/4/20 → 11/4/22

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing

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Yüksel, B., Şenaras, Ç., Özay, M., & Vural, F. Y. (2011). Yiǧilmiş genelleme yöntemiyle uydu görü ntülerinden otomatik bina bulunmasi. In 2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011 (pp. 694-697). [5929746] (2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011). https://doi.org/10.1109/SIU.2011.5929746

Yiǧilmiş genelleme yöntemiyle uydu görü ntülerinden otomatik bina bulunmasi. / Yüksel, Bariş; Şenaras, Çaǧlar; Özay, Mete; Vural, Fatoş Yarman.

2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011. 2011. p. 694-697 5929746 (2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011).

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

Yüksel, B, Şenaras, Ç, Özay, M & Vural, FY 2011, Yiǧilmiş genelleme yöntemiyle uydu görü ntülerinden otomatik bina bulunmasi. in 2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011., 5929746, 2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011, pp. 694-697, 2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011, Antalya, Turkey, 11/4/20. https://doi.org/10.1109/SIU.2011.5929746

Yüksel B, Şenaras Ç, Özay M, Vural FY. Yiǧilmiş genelleme yöntemiyle uydu görü ntülerinden otomatik bina bulunmasi. In 2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011. 2011. p. 694-697. 5929746. (2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011). https://doi.org/10.1109/SIU.2011.5929746

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abstract = "This paper proposes an automated segmentation, based algorithm for building detection in satellite images. The proposed method consists of a two layer hierarchical classification mechanism. In the first layer, each segment is classified by N different classifier according to different features. In the second layer of the mechanism, the class membership values of the segment from different first layer classifiers are concatenated to form a new vector, which is used by the meta classifier to classify the selected segment. The paper also presents the performance results of the proposed model and comparison with the single layer classifiers.",

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