Person: Leloğlu, Uğur Murat
Loading...
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Prof. Dr.
Last Name
Leloğlu
First Name
Uğur Murat
Name
Uğur LELOĞLU
6 results
Search Results
Now showing 1 - 6 of 6
Publication Vulnerability Assessment of Agriculture at Different Spatial Scales in Konya, Turkey(Common Ground Research Networks, 2022) Melike Kuş; Uğur Murat Leloğlu; Helga Rittersberger Tılıç; Leloğlu, Uğur MuratPublication A Novel Semantic Content-Based Retrieval System for Hyperspectral Remote Sensing Imagery(MDPI AG, 2024-04-20) Fatih Ömrüuzun; Yasemin Yardımcı Çetin; Uğur Murat Leloğlu; Begüm Demir; Leloğlu, Uğur MuratWith the growing use of hyperspectral remote sensing payloads, there has been a significant increase in the number of hyperspectral remote sensing image archives, leading to a massive amount of collected data. This highlights the need for an efficient content-based hyperspectral image retrieval (CBHIR) system to manage and enable better use of hyperspectral remote-sensing image archives. Conventional CBHIR systems characterize each image by a set of endmembers and then perform image retrieval based on pairwise distance measures. Such an approach significantly increases the computational complexity of the retrieval, mainly when the diversity of materials is high. Those systems also have difficulties in retrieving images containing particular materials with extremely low abundance compared to other materials, which leads to describing image content with inappropriate and/or insufficient spectral features. In this article, a novel CBHIR system to define global hyperspectral image representations based on a semantic approach to differentiate foreground and background image content for different retrieval scenarios is introduced to address these issues. The experiments conducted on a new benchmark archive of multi-label hyperspectral images, which is first introduced in this study, validate the retrieval accuracy and effectiveness of the proposed system. Comparative performance analysis with the state-of-the-art CBHIR systems demonstrates that modeling hyperspectral image content with foreground and background vocabularies has a positive effect on retrieval performance.Publication Three-dimensional surface reconstruction for cartridge cases using photometric stereo(Elsevier BV, 2008-03) Ufuk Sakarya; Uğur Murat Leloğlu; Erol Tunalı; Leloğlu, Uğur MuratPublication Automated region segmentation on cartridge case base(Elsevier BV, 2012-10) Ufuk Sakarya; Osman Topçu; Uğur Murat Leloğlu; Medeni Soysal; Erol Tunalı; Leloğlu, Uğur MuratPublication Tuz Gölü: new CEOS reference standard test site for infrared visible optical sensors(Informa UK Limited, 2010-10) Selime Gürol; Irina Behnert; Hilal Özen; Andrew Deadman; Nigel Fox; Uğur Murat Leloğlu; Leloğlu, Uğur MuratPublication A Novel Semantic Content-Based Retrieval System for Hyperspectral Remote Sensing Imagery(MDPI AG, 2024-04-20) Fatih Ömrüuzun; Yasemin Yardımcı Çetin; Uğur Murat Leloğlu; Begüm Demir; Leloğlu, Uğur MuratWith the growing use of hyperspectral remote sensing payloads, there has been a significant increase in the number of hyperspectral remote sensing image archives, leading to a massive amount of collected data. This highlights the need for an efficient content-based hyperspectral image retrieval (CBHIR) system to manage and enable better use of hyperspectral remote-sensing image archives. Conventional CBHIR systems characterize each image by a set of endmembers and then perform image retrieval based on pairwise distance measures. Such an approach significantly increases the computational complexity of the retrieval, mainly when the diversity of materials is high. Those systems also have difficulties in retrieving images containing particular materials with extremely low abundance compared to other materials, which leads to describing image content with inappropriate and/or insufficient spectral features. In this article, a novel CBHIR system to define global hyperspectral image representations based on a semantic approach to differentiate foreground and background image content for different retrieval scenarios is introduced to address these issues. The experiments conducted on a new benchmark archive of multi-label hyperspectral images, which is first introduced in this study, validate the retrieval accuracy and effectiveness of the proposed system. Comparative performance analysis with the state-of-the-art CBHIR systems demonstrates that modeling hyperspectral image content with foreground and background vocabularies has a positive effect on retrieval performance.