2014 IEEE 22nd Signal Processing and Communications Applications Conference (SIU 2014)
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HYPERSPECTRAL IMAGE VISUALIZATION BASED ON HIGH DYNAMIC
RANGE IMAGING
Seçil Süer, Hatice Koç, Sarp Ertürk
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hiperspektral veriden üç bant seçen; bir-bit G|QúP >@
normalize bilgi (normalized information, NI) [4] ve ortak bilgi
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Bu bildiride hiperspektral verinin görüntülenmesi için
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ABSTRACT
Hyperspectral imaging captures a high number of spectrally
narrow bands and provides advantages for image analysis
applications such as identification and classification in
particular. However, for the visual inspection of hyperspectral
images, the data is conventionally converted to a standard
color image format. It is important that as much detail and
data as possible is retained during this conversion. A novel
hyperspectral visualization approach based on high dynamic
range imaging is presented in this paper. The proposed
approach retains visual detail and provides a superior result
in terms of visual quality.
1.
2.
g1(5ø/(1 YÖNTEM
Bu bildiride hiperspektral verilerin görüntülenmesi için, [6]’da
önerilen \NVHNGLQDPLNDUDOÕNOÕJ|UQWOHPH\DNODúÕPÕWHPHO
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temel
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RUPDQ \DQJÕQÕ ÕVÕO KDULWDODPD SDWODPDPÕú PDGGH YH gömülü
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1168
2014 IEEE 22nd Signal Processing and Communications Applications Conference (SIU 2014)
1
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3.
DENEYSEL SONUÇLAR
Bu bildiride deneysel sonuçlar Pavia Üniversite hiperspektral
verisi (http://www.ehu.es/ccwintco/uploads/e/ee/PaviaU.mat)
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görüntüden
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LoLQ
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LPJH ùHNLO (a)’da gösterilmektedir. Renkli görüntü
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PDYL % GDOJD ER\ODUÕQD GHQN JHOHQ hiperspektral bantlar
toplanmakta ve
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Bu nedenle imge öncelikle logaritmik uzaya çevrilmektedir.
.ROD\OÕN LoLQ WDEDQOÕ ORJDULWPD NXOODQÕOPDNWD ROXS EX
nedenle logaritmik uzaydaki imge
ܴ‫ = ܴܦܪ‬σ‫)ݐ(ܫܪ ܴאݐ‬
‫ = ܴܦܪܩ‬σ‫)ݐ(ܫܪ ܩאݐ‬
‫ = ܴܦܪܤ‬σ‫)ݐ(ܫܪ ܤאݐ‬
(3)
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taban imgesi
‫ ݃݋݈ܫ‬,‫ = ܾ݊ܽܽݐ‬Çܻܵ൫‫ ݃݋݈ܫ‬൯
(7)
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(8)
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edilmektedir. Burada HI hiperspektral imgeyi göstermekte
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LOH EX o EDQW +6, X]D\ÕQD oHYULOPHNWH YH EX VD\HGH UHQN
bilgisi ile \NVHN GLQDPLN DUDOÕNOÕ ÕúÕNOÕOÕN ELOJLVL
(‫ ) ܴܦܪܫ‬D\UÕúWÕUÕOPDNWDGÕU %LUVRQUDNLDúDPDGDLVH%|OP¶GH
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GLQDPLNDUDOÕNOÕÕúÕNOÕOÕNELOJLVL‫ ) ܴܦܮܫ‬elde edilmektedir.
(4)
0T
RODUDN ROXúWXUXOPDNWDGÕU %XUDGD d<6
süzgeçlemeyi ifade etmektedir. Detay imgesi ise
‫ ݃݋݈ܫ‬,݀݁‫ ݃݋݈ܫ = ݕܽݐ‬െ ‫ ݃݋݈ܫ‬,‫ܾ݊ܽܽݐ‬
(6)
oLIW-yönlü
0T
(5)
1169
2014 IEEE 22nd Signal Processing and Communications Applications Conference (SIU 2014)
4.
SONUÇ
Bu bildiride hiperspektral J|UQWOHULQ J|UVHOOHúWLULOPHVL LoLQ
\HQL ELU \DNODúÕP |QHULOPHNWHGLU gQHULOHQ \DNODúÕP
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\|QWHPLQH GD\DQPDNWDGÕU +LSHUVSHNWUDO EDQWODUÕQ WRSODQPDVÕ
sonucunda bir nevi bir yüksek dinamik aralÕNOÕ YHUL
ROXúWXUXOPDNWD YH VRQUDVÕQGD X\JXQ ELU NDUúÕWOÕN D]DOWÕPÕ
\DNODúÕPÕ\OD VRQ J|UQW HOGH HGLOPHNWHGLU gQHULOHQ
\|QWHPLQ D\UÕQWÕODUÕQ J|VWHULPL DoÕVÕQGDQ EDúDUÕOÕ YH J|UVHO
DOJÕ\DX\JXQELUVRQXoYHUGL÷LJ|VWHULOPLúWLU
5.
(a)
(b)
[1] H. Du, H. Qi, X. Wang, and R. Ramanath, “Band
selection using independent component analysis for
hyperspectral image processing,” in Proc. 32nd Appl.
Imagery Pattern Recog. Workshop, Washington, DC, pp.
93–99, Oct. 2003.
[2] X. Jia and J. A. Richard, “Segmented principal
components transformation for efficient hyperspectral
remote-sensing image display and classification,” IEEE
Trans. Geosci. Remote Sens., vol. 37, no. 1, pp. 538–542,
Jan. 1999
[3] B. Demir, A. Celebi, and S. Erturk, “A low-complexity
approach for the color display of hyperspectral remotesensing images using one-bit transform-based band
selection,” IEEE Trans. Geosci. Remote Sens., vol. 47,
no. 1, pt. 1, pp. 97–105, 2009.
[4] S. Le Moan, A. Mansouri, Y. Voisin, and J. Y.
Hardeberg, “A constrained band selection method based
on information measures for spectral image color
visualization,” IEEE Trans. Geosci. Remote Sens., vol.
49, no. 12, pp. 5104–5115, 2011.
[5] B. Guo, S. R. Gunn, R. I. Damper, and J. B. Nelson,
“Band selection for hyperspectral image classification
using mutual information,” IEEE Geosci. Remote Sens.
Lett., vol. 3, no. 4, pp. 522–526, 2006.
[6] F. Durand, and J. Dorsey, “Fast Bilateral Filtering for the
Display of High-Dynamic-Range Images”, ACM
Transactions on Graphics - Proceedings of ACM
SIGGRAPH 2002 vol. 21, no. 3, pp. 257-266, Jul. 2002.
[7] C. Tomasi, and R. Manduchi, “Bilateral filtering for gray
and color images.” In Proc. IEEE Int. Conf. on Computer
Vision, pp. 836–846, 1998.
(c)
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basit J|UVHOOHúWLUPHFgQHULOHQ\|QWHPLOH
J|UVHOOHúWLUPH
øOJLOL G|QúP UHQNOL LPJH\H \DQVÕWPDN LoLQ EDVLW ELU SLNVHO
ED]OÕRUDQPDWULVL
‫ ܴܦܮܫ = ݊ܽݎ݋‬/‫ܴܦܪܫ‬
(9)
úHNOLQGH HOGH HGLOPHNWHGLU %X RUDQ NXOODQÕODUDN \HQL UHQN
NDQDOODUÕ
ܴ‫ܴܦܪܴ × ݊ܽݎ݋ = ܴܦܮ‬
‫ܴܦܪܩ × ݊ܽݎ݋ = ܴܦܮܩ‬
‫ܴܦܪܤ × ݊ܽݎ݋ = ܴܦܮܤ‬
KAYNAKÇA
(10)
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NXOODQÕOPDVÕ\OD HOGH HGLOHQ UHQNOL LPJH LVH ùHNLO F¶GH
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1170

AC 2250

Catalog