Yıl 2020, Cilt 7 , Sayı 1, Sayfalar 93 - 101 2020-04-26

In hyperspectral images, pixels are found as a mixture of the spectral signatures of several materials, especially when there is an insufficient spatial resolution. In recent years, spectral libraries have provided spectral information of hundreds of materials that allow the development of techniques to solve the problem of hyperspectral unmixing in a semi-supervised fashion. These methods which are also known as sparse regression techniques assume that mixed pixels are a sparse linear combination of spectral signatures of materials in already available spectral libraries. In this paper, the spectral mixing problem has been solved via sparse separation methods. The United States Geological Survey (USGS) spectral library is used to generate simulated hyperspectral data. A comparative analysis is performed to determine which material signatures in the library are mixed in the pixels by using the convex-relaxation-based sparse regression methods. Root Mean Square Error (RMSE), Signal to Reconstruction Error (SRE) and processing time of the algorithms are used as comparing criterions. Moreover, Hinton diagrams are used to visualize which material signatures are found in the library and the proportions of these found material signatures.

: Hyperspectral Imaging, Hyperspectral Unmixing, Sparse Regression, Sparse Unmixing, Spectral Library
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Birincil Dil en
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Orcid: 0000-0002-0279-3185
Yazar: Sefa KÜÇÜK (Sorumlu Yazar)
Kurum: Hacettepe University, Department of Electrical and Electronics Engineering
Ülke: Turkey


Orcid: 0000-0000-0000-0000
Yazar: Seniha Esen YÜKSEL
Kurum: Hacettepe University, Department of Electrical and Electronics Engineering
Ülke: Turkey


Tarihler

Yayımlanma Tarihi : 26 Nisan 2020

Bibtex @araştırma makalesi { ijegeo649394, journal = {International Journal of Environment and Geoinformatics}, issn = {}, eissn = {2148-9173}, address = {}, publisher = {Cem GAZİOĞLU}, year = {2020}, volume = {7}, pages = {93 - 101}, doi = {10.30897/ijegeo.649394}, title = {Unmixing of Hyperspectral Data Using Spectral Libraries}, key = {cite}, author = {KÜÇÜK, Sefa and YÜKSEL, Seniha Esen} }
APA KÜÇÜK, S , YÜKSEL, S . (2020). Unmixing of Hyperspectral Data Using Spectral Libraries. International Journal of Environment and Geoinformatics , 7 (1) , 93-101 . DOI: 10.30897/ijegeo.649394
MLA KÜÇÜK, S , YÜKSEL, S . "Unmixing of Hyperspectral Data Using Spectral Libraries". International Journal of Environment and Geoinformatics 7 (2020 ): 93-101 <https://dergipark.org.tr/tr/pub/ijegeo/issue/53413/649394>
Chicago KÜÇÜK, S , YÜKSEL, S . "Unmixing of Hyperspectral Data Using Spectral Libraries". International Journal of Environment and Geoinformatics 7 (2020 ): 93-101
RIS TY - JOUR T1 - Unmixing of Hyperspectral Data Using Spectral Libraries AU - Sefa KÜÇÜK , Seniha Esen YÜKSEL Y1 - 2020 PY - 2020 N1 - doi: 10.30897/ijegeo.649394 DO - 10.30897/ijegeo.649394 T2 - International Journal of Environment and Geoinformatics JF - Journal JO - JOR SP - 93 EP - 101 VL - 7 IS - 1 SN - -2148-9173 M3 - doi: 10.30897/ijegeo.649394 UR - https://doi.org/10.30897/ijegeo.649394 Y2 - 2020 ER -
EndNote %0 International Journal of Environment and Geoinformatics Unmixing of Hyperspectral Data Using Spectral Libraries %A Sefa KÜÇÜK , Seniha Esen YÜKSEL %T Unmixing of Hyperspectral Data Using Spectral Libraries %D 2020 %J International Journal of Environment and Geoinformatics %P -2148-9173 %V 7 %N 1 %R doi: 10.30897/ijegeo.649394 %U 10.30897/ijegeo.649394
ISNAD KÜÇÜK, Sefa , YÜKSEL, Seniha Esen . "Unmixing of Hyperspectral Data Using Spectral Libraries". International Journal of Environment and Geoinformatics 7 / 1 (Nisan 2020): 93-101 . https://doi.org/10.30897/ijegeo.649394
AMA KÜÇÜK S , YÜKSEL S . Unmixing of Hyperspectral Data Using Spectral Libraries. International Journal of Environment and Geoinformatics. 2020; 7(1): 93-101.
Vancouver KÜÇÜK S , YÜKSEL S . Unmixing of Hyperspectral Data Using Spectral Libraries. International Journal of Environment and Geoinformatics. 2020; 7(1): 101-93.