Radar Remote Sensing. Contents of Presentation. Henning Skriver Digital Image Analysis, Vision and Computer Graphics Fall 2008.

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1 Radar Remote Sensing Henning Skriver Digital Image Analysis, Vision and Computer Graphics Fall 2008 Contents of Presentation SAR Techniques SAR Polarimetric SAR Interferometric SAR Image Processing Techniques Speckle reduction Classification Edge Detection Segmentation Change detection 1

2 Contents of Presentation SAR Techniques SAR Polarimetric SAR Interferometric SAR Image Processing Techniques Speckle reduction Classification Edge Detection Segmentation Change detection Earth Observation - Principles 2

3 Absorption in the atmosphere 3

4 Side-Looking Airborne Radar Flight track Antenna v (Antenna velocity) direction)! nr (Near-range incidence) angle) x (Along-track direction) R S (Slant-range swath) y (Across-track direction) Pulse radar 4

5 SLAR - azimuth Radar Radar Radar Radar EMISAR 5

6 ENVISAT Dimensions Launch configuration: length 10.5 m envelope diameter 4.6 m In-Orbit configuration: 26m x 10m x 5m Mass Total satellite 8140 Kg Payload 2050 Kg Power Solar array power: 6.5 kw (EOL) Average power demand: Sun Eclipse (watts) (watts) Payload Satellite Orbit 800 km as ERS, sun synchronous 10:00, i.e. 30 minutes before ERS-2 6

7 Surface scattering Specular reflection Rough surface scattering 7

8 8

9 Flooding by radar NOAA AVHRR 9

10 Contents of Presentation SAR Techniques SAR Polarimetric SAR Interferometric SAR Image Processing Techniques Speckle reduction Classification Edge Detection Segmentation Change detection 10

11 Polarimetric SAR Scattering matrix! # " S vv S hv S vh S hh $ & % Polarimetric SAR 11

12 12

13 EMISAR C- and L-band Multitemporal C-band HH HV VV L-band March May July Contents of Presentation SAR Techniques SAR Polarimetric SAR Interferometric SAR Image Processing Techniques Speckle reduction Classification Edge Detection Segmentation Change detection 13

14 Interferometric SAR H 1 2 Elevation mapping R R +!R EMISAR 14

15 Interferometric SAR H 1 2 Elevation mapping R R +!R H 1/2 Displacement/ velocity R " 12!R 15

16 Terrain Motion in L.A., USA: today 16

17 Contents of Presentation SAR Techniques SAR Polarimetric SAR Interferometric SAR Image Processing Techniques Speckle reduction Classification Edge Detection Segmentation Change detection Speckle 17

18 Speckle Speckle 18

19 Speckle Speckle 19

20 Speckle Reduction Speckle Reduction 20

21 Contents of Presentation SAR Techniques SAR Polarimetric SAR Interferometric SAR Image Processing Techniques Speckle reduction Classification Edge Detection Segmentation Change detection Polarimetric SAR Scattering matrix! # " S vv S hv S vh S hh $ & % 21

22 EMISAR C- and L-band Multitemporal C-band HH HV VV L-band March May July EMISAR L-band Multitemporal Correlation coefficient 1 0 Phase difference March May July 22

23 Polarimetric SAR - pdf s Scattering matrix " S = S hh $ # $ S vh S hv S vv % ' &' Z = [ S hh S hv S vv ] T " $ X = ZZ T $ = $ $ # $ Covariance matrix S hh S hh S hv S hh S vv S hh S hh S hv S hv S hv S vv S hv S hh S vv S hv S vv S vv S vv % ' ' ' ' &' Complex Gaussian Z " N C (0,#) u(z) = 1 " p # exp { $tr(#$1 zz T )} Complex Wishart X " W C (p,n, #) 1 w(x) = x N$p " p (N) # N exp { $tr(# $1 x) } Gamma I " G(N,#) 1 v(i) = "(N)# I % N$1 exp $ I ) & N '( # + ( Complex Wishart classification Multidimensional ML classification Complex Wishart classification u = [ u 1 u 2 L u n ] " $ x = zz T = $ $ # S hh S hh S hv S hh S vv S hh S hh S hv S hv S hv S vv S hv S hh S vv S hv S vv S vv S vv % ' ' ' & ( ) = p u 1 2" n C 1 2 exp(# 1 ( u # u 2 )C#1 (u # u )) 1 w(x) = " p (N) # x N$p N exp { $tr(# $1 x) } d 1 (u,class m ) = 1 ( u " u 2 )C"1 (u " u ) + 1 ln C " ln P(class 2 [ ) m ] d 3 (x,class m ) = n Tr(" #1 x) +n ln " # ln[ P(class m )] 23

24 Land cover from radar Contents of Presentation SAR Techniques SAR Polarimetric SAR Interferometric SAR Image Processing Techniques Speckle reduction Classification Edge Detection Segmentation Change detection 24

25 Edge Detection Scheme SW-NE W-E What is edge detection? Statistical test of the hypothesis: Mean[RED area] = Mean[BLUE area]? If hypothesis is rejected: We have an edge! Edge Detection Scheme X 11 X 12 X 13 X 21 X 22 X 23 X 31 X 32 X 33 Test for edge using test statistic f: N-S edge: E N-S = f(x 11 +X 21 +X 31, X 13 +X 23 +X 33 ) NW-SE edge: E NW-SE = f(x 12 +X 13 +X 23, X 13 +X 23 +X 33 ) W-E edge: E W-E = f(x 11 +X 12 +X 13, X 31 +X 32 +X 33 ) SW-NE edge: E SW-NE = f(x 21 +X 11 +X 12, X 32 +X 33 +X 23 ) Edge enhancement and direction: Is the hypothesis of equal means rejected by 1 of E s Examples SW-NE W-E 25

26 Edge Detection - Gaussian X 11 X 12 X 13 X 21 X 22 X 23 X 31 X 32 X 33 Test statistic when pixels are Gaussian distributed: X i N(µ i,σ i ) f(x,y) " $ X i # $ Y i Sum[RED area] - Sum[BLUE area] Polarimetric SAR - pdf s Scattering matrix " S = S hh $ # $ S vh S hv S vv % ' &' Z = [ S hh S hv S vv ] T " $ X = ZZ T $ = $ $ # $ Covariance matrix S hh S hh S hv S hh S vv S hh S hh S hv S hv S hv S vv S hv S hh S vv S hv S vv S vv S vv % ' ' ' ' &' Gamma I " G(N,#) 1 v(i) = "(N)# I % N$1 exp $ I ) & N '( # + ( 26

27 Edge Detection - Gamma X 11 X 12 X 13 X 21 X 22 X 23 Test statistic when pixels are Gamma distributed: X i G(N,β i ) X 31 X 32 X 33 # f(x,y) " X i #Y i Sum[RED area] Sum[BLUE area] Polarimetric SAR - pdf s Scattering matrix " S = S hh $ # $ S vh S hv S vv % ' &' Z = [ S hh S hv S vv ] T " $ X = ZZ T $ = $ $ # $ Covariance matrix S hh S hh S hv S hh S vv S hh S hh S hv S hv S hv S vv S hv S hh S vv S hv S vv S vv S vv % ' ' ' ' &' Complex Wishart X " W C (p,n, #) 1 w(x) = x N$p " p (N) # N exp { $tr(# $1 x) } Gamma I " G(N,#) 1 v(i) = "(N)# I % N$1 exp $ I ) & N '( # + ( 27

28 Wishart Edge Detector X 11 X 12 X 13 X 21 X 22 X 23 Test statistic for complex Wishart pdf X i W C (p,n,σ i ) X 31 X 32 X 33 N $ #X i #Y i f(x, Y) " # X i + # Y i M $ N $ + M $ Sum[RED area] N Sum[BLUE area] M Sum[RED area]+sum[blue area] N+M EMISAR L-band HH HV VV Phase diff. HH VV Corr. coef. HH VV

29 Wishart Edge Detector - L-band diagonal L-band L-band diagonal Wishart Edge Det. - L-band az. sym. L-band L-band azimuthal symmetric 29

30 EMISAR L-band HH HV VV Phase diff. HH VV Corr. coef. HH VV Contents of Presentation SAR Techniques SAR Polarimetric SAR Interferometric SAR Image Processing Techniques Speckle reduction Classification Edge Detection Segmentation Change detection 30

31 Segmentation Merge Red and Blue regions if hypothesis of equal means is accepted Segments for Polarimetric SAR 31

32 Azimuthal Symmetric - Diagonal Contents of Presentation SAR Techniques SAR Polarimetric SAR Interferometric SAR Image Processing Techniques Speckle reduction Classification Edge Detection Segmentation Change detection 32

33 Change Detection Acquisition 1 Acquisition 2 Change has occurred between acq. 1 and acq. 2, if hypothesis of equal means for red and blue areas is rejected June 98, XP, L-band June 99, XP, L-band 33

34 June 98, XP, L-band Difference detector June 98, XP, L-band Ratio detector 34

35 June 98, L-band June 99, L-band June 98, XP, L-band Ratio detector 35

36 June 98, L-band Wishart detector Segmentation af 2 images separately Acquisition 1 Acquisition 2 Cov. matrix X 1 Cov. matrix X 2 36

37 Segmentation af 2 images jointly Acquisition 1 Acquisition 2 Covariance matrix for 2 images: " X = X 1 0 % $ ' # 0 X 2 & June 98, L-band June 99, L-band 37

38 June 98, L-band June , L-band Pixel-based test statistics Segment-based test statistics 38

39 Pixel-based test statistics Segment-based test statistics Freeman and Durden decomposition 39

40 Only double-bounce scattering Only double-bounce scattering 40

41 41

42 42

43 DTU - courses Remote Sensing Radar and Radiometer Systems 43

Monitoring a Changing Environment with Synthetic Aperture Radar. Alaska Satellite Facility National Park Service Don Atwood

Monitoring a Changing Environment with Synthetic Aperture Radar Don Atwood Alaska Satellite Facility 1 Entering the SAR Age 2 SAR Satellites RADARSAT-1 Launched 1995 by CSA 5.6 cm (C-Band) HH Polarization