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1 !"#$%&'()'*+,-.%/'01)2)' 333)45%6)6+&"4.%-7)#89:;7+,-.%'

2 !"#$%&#$'(&)*#$*+,-$!! <-&'$%4"'=>'5#4+&85?'!!

3 ."+*$(/$'),*(/0#1*2+,3$!! @7-56'#J#4")''!! 31-"#'.-K1")''

4 450#2/0#1*2+,$6'+7#25$!! Multispectral Many spectra (bands)!! Hyperspectral Huge numbers of continuous bands Increasing Wavelength (in meters) Gamma Rays 10-8 Ultraviolet 10-6 Infrared 10 Radio X-Rays Visible Electromagnetic Spectrum Microwaves 10-2

5 450#2/0#1*2+,$8$9),*(/0#1*2+,$ Multispectral each pixel has several large discrete spectral bands Hyperspectral each pixel has many small continuous spectral bands R!" R!"

6 450#2/0#1*2+,$6'+7#25$!! Hyperspectral remote sensing provides a continuous, essentially complete record of spectral responses of materials over the wavelengths considered

7 Flight Line Intensity Pixel Spectrum Single Pixel Wavelength Spatial Pixels Spectral Bands Series of Sensor Frames Single Sensor Frame

8 :#&/%2/$

9 Types of HSI Resolution L1#'56%..#5"'8-5"%&4#',#"3##&'"3+'+,O#4"5'%"' 8-5"-&4"' L1#'%,-.-"B'+$'%'5#&5+7'"+'8-5"-&K9-51',#"3##&' D%7-+95'3%D#.#&K"15)''L1#'6+7#',%&85/'"1#' L1#'8#K7##'"+'31-41'%'5B5"#6'4%&'7#4+78'.#D#.5'

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

11 ;,%&7<*2+1-$:1+&&#2/$!! (.5+'H&+3&'%5'0X!RT*UUV'!4%&&#75'!!!#&5#'%'53%"1'3-"1'%&'.-&#%7'%77%B'+$'PP2Y5' "! "1#-7'6#41%&-4%.'7#.-%,-.-"B'4%&',#'D#7B'1-K1)' D$"-3)%#.26&)+%+)7'#)*1%#"&&)+%2231%"-)%/.*0&>% "+.60)+%,.-%&'()"-%"--">%*)(*.-*4%<$)-),.-)%'0%'*% *./)0'/)%#"&&)+%"%&'()"-%DDE%*)(*.-%.-%DDE% #"/)-"4%%

12 :%'#$+(2=%2&#$/5/*#'/$ Sensor! Country! Nr bands! Range µm! AVIRIS! USA! 224! ! AISA! FI! 286! ! CASI! CA! 288! ! DAIS! USA! 211! ! HYMAP! AU! 128! ! PROBE1! USA! 128! !

13 :+**#,(*#$/#&/%2/$

14 >'#27(&7$?(2#1*(%&/$!!!"%7-&K'!4%&&#75' +&#'-&5"%&"%&#+95'D-8#+'$7%6#)'L1-5'#.-6-&%"#5'6+"-+&'%7"-$%4"5' %&8'6%N-6-]#5'5-K&%.G"+G&+-5#)^'<,+8H-&8#5-K&)4+6?' 7%8-+6#"7-4%..B'4%.-,7%"#8'3-"1'5+$"3%7#'$+7'%&%.B5-5'%&8'D-#3-&K)' M-`9-8'L9&%,.#'d-."#75 '' 14

15 9%2#$%&$:#&/%2/3$$ Here is a reasonable presentation: Here is an expanded list: Paul Geladi feb 06

16 ;00,(1+*(%&/$$!! V%"#7-%.'F8#&"-e4%"-+&'!! R+6#.%&8'!#497-"B'!! f&d-7+&6#&"%.'<3#".%&85/'.%&8'4+d#7/'1b87+.+kb/' #"4)?'!! R#%."1'P%7#'<$++8'5%$#"B/'6#8-4%.'8-%K&+5#5/'#"4)?'!! M-""+7%.'!"98-#5'<,%"1B6#"7B/'3%"#7'4.%7-"B/'#"4)?'!! L7%g4%,-.-"B'(&%.B5-5'!! M%&8'V-&#'2#"#4"-+&'!! 0.96#'(&%.B5-5'!! P%6+9h%K#/'P+&4#%.6#&"/'2#"#4"-+&'!! T-+.+K-4%.'%&8'P1#6-4%.'2#"#4"-+&'!! 07#4-5-+&'(K7-49."97#:d%76-&K'!! 2-5%5"#7'V-"-K%"-+&'!! P-"B'0.%&&-&K'%&8'*#%.'f5"%"#'!! M%3'f&$+74#6#&"'!! V%&B'U"1#75'

17

18 "! M%7K#'8%"%'5-]#' "! 0++7'5-K&%.G"+G&+-5#'7%"-+5' "! "! "! 4.%55-e4%"-+&'

19 P.%55-e4%"-+&'!! d#%"97#'fn"7%4"-+&'

20 :0#1*2+,$9+*1"(&7$!! Hyperspectral remote sensing provides a continuous, essentially complete record of spectral responses of materials over the wavelengths considered.

21 :0#1*2+,$9#*2(1/$!! Hyperspectral remote sensing provides a continuous, essentially complete record of spectral responses of materials over the wavelengths considered.

22

23 :0#1*2+,$C&'(D(&7$ Spectral unmixing, Keshava, N.; Mustard, J.F., Signal Processing Magazine, IEEE, Vol.19, Iss.1, Jan 2002, Pages:44-57

24 Each n-dimensional observed pixel vector x can be expressed as: S is the nxm matrix of spectra (s 1,.., s m ) endmembers a is an m-dimensional vector - abundance vector w is the additive noise vector The elements of the abundance vector are assumed to be positive and with unit sum: (6) (5) (7) Linear Unmixing - find the endmembers and their abundances.

25 ."5$('0%2*+&*3$!! (..+35'-8#&"-e4%"-+&'+$'6%"#7-%.5'%&8' #5"-6%"-+&'+$'`9%&"-"-#5)'!!

26 "! "! "! "! "!

27 E:9;$!! "! "! 54#&#)' "! L1#'&96,#7'+$'#&86#6,#75'4%&',#'8#"#76-&#8)'!! "! f&86#6,#7'8#"#4"-+&'!! "! (,9&8%&4#'#5"-6%"-+&'!! M#%5"'5`9%7#'6#"1+8'<M!f/'!PM!/'kPM!/'dPM!?'!! L%7K#"'4+&5"7%-&#8'6#"1+8'<PfV?'

28 A%&H#D$I#%'#*25$!! P+&D#N'19..' "! "! "! f&86#6,#7'8#"#4"-+&'-5'' ''''#`9-D%.#&"'"+'-8#&"-$B-&K'D#7"-4#5)' Convex hull in R 2 Simplex in R 2

29 J%&&#7+*(H#$9+*2(D$K+1*%2(L+*(%&$FJ9KG$ Given the observed data x, the goal of NMF is to find s and a linear mixing transform W both positively defined such that: x=ws (8) This approach can be understood as factorizing a data matrix subject to positive constraints. (9)

30 J9K$:%,)*(%&$!Constrain positivity!optimize based on gradient: (10) (11) (12) (function based on the Euclidean norm)

31 >D+'0,#/$ Surface Optics (SOC 700) artificial and natural vegetation!120 bands with wavelengths between 400nm and 900nm

32 >D0#2('#&*/$

33 >D0#2('#&*/$ Hyperspectral Digital Imagery Collection Experiment (HYDICE)!Foliage scene!spatial resolution of 1.5m!210 bands with wavelengths between 400nm and 2.5 micron.!rows of panels made of 8 different materials!sizes 1mx1m, 2mx2m, 3mx3m!Small forest patch!exposed ground

34

35 n n!! 07-&4-@%.'P+6@+&#&"'(&%.B5-5'!! F&8#@#&8#&"'P+6@+&#&"'(&%.B5-5'!! +"1#7o'

36 B2(&1(0+,$A%'0%&#&*$;&+,5/(/$FBA;G$ For the multidimensional random vector x, PCA finds a linear transform W such that the obtained components are uncorrelated: The transform is obtained as: Y=Wx (1) W= A x T (2) Where A x is the matrix formed of the normalized eigenvectors for the covariance matrix! x.

37 B2(&1(0+,$A%'0%&#&*$;&+,5/(/$ P1 PCT P2 P3 Color mapping Composite image Source images Transformed images (Achalakul 2000)

38 ;002%+1"#/$ Independent Component Analysis (ICA) Given the n dimensional random vector x, the goal is to find the &N6 linear transform W such that the resulting components of!e"# are independent from each other x n ICA (W) u m! Investigation of models of the hyperspectral data for ICA! Design of ICA based feature extraction algorithms

39 Given the n dimensional random vector x, the goal is to find the nxm linear transform W such that the resulting components of u=wx are independent from each other.

40 A,+//(N(1+*(%&$

41 Adapt current (or develop new) algorithms to environments that use multiple processors, memory, etc.!! The algorithm introduces a speedup close to linear on the number of worker processes generated.!! Several bottleneck stages, such as subcube generation and communication, introduce overhead that affects the performance.!! The preliminary experimental results support the initial assumptions.

42 B+2+,,#,$J9K$

43 O#+,$!('#$B2%1#//(&7$ Design real time systems capable of processing hyperspectral data:

44 O#+,$!('#$B2%1#//(&7$

45 K+1#$O#1%7&(*(%&$

46 F&8++7'%&8'+9"8++7'54#&#5'

47 Average chin spectral angle for each of the subject pair combinations. This was obtained by taking the average individual angles for each location (i.e. spectral angle between the average subject 1 chin spectra, and the chin spectra of each other subject).

48 K)*)2#$?(2#1*(%&/$!! Extend beyond traditional RS think outside the box:!! Archimedes Palimpsest 10Th century manuscript written over in the 12Th century

49 K)*)2#$?(2#1*(%&/$!! "! `9%.-"B'4+&"7+.' "! 41#6-4%.'6+&-"+7-&K' "! 1#%."1'6+&-"+7-&K' "! 4+9&"#7$#-"'8#"#4"-+&'

50

51 A%&1,)/(%&$!! -&47#%5-&K'&96,#7'+$'e#.85'!!!#&5+7'8#5-K&'-5'5##-&K'%'5-K&-e4%&"'%8D%&4#'-&' 7#5+.9"-+&'-&'%..'8-7#4"-+&5'!! T9"o'

52 !"#2#$(/$2%%'$N%2$('02%H#'#&*$

53 K)*)2#3$<$6&N%2'+*(%&$K)/(%&$ Sensor fusion is the combining of sensory data such that the resulting information is in some sense better than would be possible when these sources were used individually. Approaches: -!Mutual information -!Segment alignment -!Principal Component Merge -!Wavelet based Fusion Etc.

54 It was six men of Hindustan To learning much inclined, Who went to see the Elephant (Though all of them were blind) That each by observation Might satisfy the mind. The first approached the Elephant And happening to fall Against his broad and sturdy side At once began to bawl: "Bless me, it seems the Elephant Is very like a wall".. And so these men of Hindustan Disputed loud and long, Each in his own opinion Exceeding stiff and strong, Though each was partly in the right And all were in the wrong.