Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Okan Bilge Ozdemir"'
Autor:
Mustafa Kutuk, Izlen Geneci, Okan Bilge Ozdemir, Alper Koz, Okan Esenturk, Yasemin Yardimci Cetin, A. Aydin Alatan
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 8747-8763 (2023)
Explosive detection is crucial for public safety and confidence. Among various solutions for this purpose, hyperspectral imaging differs from its alternatives with its detection capability from standoff distances. However, the state-of-the-art for su
Externí odkaz:
https://doaj.org/article/be8c2c83451f4e64abdae50de3ad86a2
Autor:
Okan Bilge Ozdemir, Alper Koz
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 1474-1482 (2023)
The detection of gas emission levels is a crucial problem for ecology and human health. Hyperspectral image analysis offers many advantages over traditional gas detection systems with its detection capability from safe distances. Observing that the e
Externí odkaz:
https://doaj.org/article/d18775ae726c4dd1be8fd00d1012e01a
Publikováno v:
Remote Sensing; Volume 12; Issue 23; Pages: 3931
Remote Sensing, Vol 12, Iss 3931, p 3931 (2020)
Remote Sensing, Vol 12, Iss 3931, p 3931 (2020)
Hyperspectral image processing techniques, with their ability to provide information about the chemical compositions of materials, have great potential for pavement condition assessment. This study introduces a novel age-based pavement assessment met
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::384cfd73b165af91a9e49142cc705adf
Publikováno v:
SPIE Proceedings.
Hyperspectral images have been used in many areas including city planning, mining and military decision support systems. Hyperspectral image analysis techniques have a great potential for vegetation detection and classification with their capability
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6205f2aba4524a90339512b9fa440b26
https://doi.org/10.1117/12.2262480
https://doi.org/10.1117/12.2262480
Publikováno v:
IGARSS
This paper presents a vegetation detection application with semi-supervised target detection using hyperspectral unmixing and segmentation algorithms. The method firstly compares the known target spectral signature from a generic source such as a spe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b8398e59bf482fa144f44d76e0652673
https://doi.org/10.1109/igarss.2016.7730825
https://doi.org/10.1109/igarss.2016.7730825
Publikováno v:
IGARSS
The aim of this study is to the evaluate land use and the land cover changes of an abandoned coal mine in Central Anatolia. The mining activity in the region was started at 1987 and after working for 18 years, all the rights of the mine was passed to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::61f99a6cb596564c4ab2fdd36d705dff
https://doi.org/10.1109/igarss.2015.7326459
https://doi.org/10.1109/igarss.2015.7326459
Publikováno v:
SIU
In this study, the contribution of utilizing hyperspectral unmixing algorithms on signature based target detection algorithms is studied. Spectral Angle Mapper (SAM), Spectral Matched Filter (SMF) and Adaptive Cosine Estimator (ACE) algorithms are se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::802ca20f340415e7619f9d11166141aa
https://doi.org/10.1109/siu.2015.7130392
https://doi.org/10.1109/siu.2015.7130392
Publikováno v:
WHISPERS
In this study, stacked autoencoders which are widely utilized in deep learning research are applied to remote sensing domain for hyperspectral classification. High dimensional hyperspectral data is an excellent candidate for deep learning methods. Ho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6a7f63080665c4f50fb0e5506e6e0c0d
https://doi.org/10.1109/whispers.2014.8077532
https://doi.org/10.1109/whispers.2014.8077532
Publikováno v:
SIU
In this study, the performance of hyperspectral classification algorithms with limited training data investigated. Support Vector Machines (SVM) with Gaussian kernel is used. Principle Component Analysis (PCA) is employed for preprocessing and meansh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eeeed01ccca1c8cbcaa56a33c0e3e342
https://doi.org/10.1109/siu.2014.6830599
https://doi.org/10.1109/siu.2014.6830599
Publikováno v:
WHISPERS
This study investigates the effect of training set selection strategy on classification accuracy of hyperspectral images. This effect is analyzed in conjunction with three other factors, namely the use principal component analysis on the input data,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::04307f19fae8737fe4b6f60ab957d720
https://doi.org/10.1109/whispers.2013.8080648
https://doi.org/10.1109/whispers.2013.8080648