Ten years of remote sensing advancement & the research outcome of the CRC-AGIP Lab
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1 Ten years of remote sensing advancement & the research outcome of the CRC-AGIP Lab Dr. Yun Zhang Canada Research Chair Laboratory in Advanced Geomatics Image Processing (CRC-AGIP Lab) Department of Geodesy and Geomatics Engineering University of New Brunswick (UNB) CRC-AGIP 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 1
2 Content: 1. Remote Sensing Advancement 2. AGIP Lab Technologies a. Image Fusion b. Adjustable SAR-MS Fusion c. Moving Target Detection d. Image Matching e. Image Segmentation f. InSAR DEM Reconstruction g. Online 3D h. Generic RPC sensor model refinement 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 2
3 1. Remote Sensing Advancement 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 3
4 Optical earth observation satellites Optical satellite Spatial resolution (m) (# of bands) Swath (km) Year of Pan* MS* launch VNIR* SWIR* TIR* Landsat (4) 30 (2) 60 (1) CBERS 1 and (4) , 2003 Ikonos (4) Terra/ASTER 15 (3) 30 (6) 90 (5) KOMPSAT EROS A Quickbird (4) SPOT (3) 20 (1) IRS-P6 / ResourceSat (3), 23.5 (3) 24, 70, DMC-AlSat1 32 (3) DMC-BILSAT (4) 25, DMC-NigeriaSat 1 32 (3) UK-DMC 32 (3) OrbView (4) DMC-Beijing (3) 24, TopSat (3) KOMPSAT (4) IRS-P5/CartoSat ALOS (4) 35, Resurs DK (3) WorldView RazakSat (4) RapidEye A E (5) [Zhang and GeoEye (4) Kerle, 2007; EROS B C Stoney, 2008] WorldView (8) Plèiades-1 and (4) , 2011 CBERS 3 and (4), (2) 80 60, , /8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 4
5 Airborne digital cameras/sensors Brand Name Date of update Weight [kg] # of lenses # of CCD a chips # of pixels across track # of pixels along track Spectral bands b DSS kg 1 1 5,436 4,092 R,G,B or Applanix NIR,R,G DSS kg 1 1 7,216 5,412 R,G,B or NIR,R,G DIMAC DiMAC kg 2 to 4 2 to 4 10,500 7,200 R,G,B, NIR IGI DigiCAM- H/39 DigiCAM- H/ kg 1 1 7,216 or 5, kg ,500 or 10,000 5,412 or 7,216 R,G,B, or NIR 10,000 or 13,500 R,G,B, or NIR Intergraph DMC kg ,824 7,680 Pan, R,G,B, NIR Jena JAS 150s kg ,000/line Unlimited Pan, R,G,B, NIR Leica ADS kg 1 8 or 12 12,000/line Unlimited Pan, R,G,B, NIR RolleiMetric AIC x kg 1 1 5,440 or 4,080 7,228 or 5,428 RGB or IR AIC x kg no lenses ,227 or 4,080 AIC x kg ,227 or 7,670 13,588 or 5,428 13,588 or 10,204 RGB and IR / RGB or IR RGB and IR / RGB or IR Vexcel UltraCam X kg ,430 (pan) 9,420 (pan) Pan, R, G, B, NIR Wehrli 3-OC kg 3 3 8,002/line Unlimited R,G,B [GIM International, 2008] 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 5
6 Radar earth observation satellites Satellite Sensor Year of launch Band Wavelengt h (cm) Polarizatio n Resolution range (m) Resolutio n azim. (m) Scene width (km) ERS-1 AMI 1991 C 5.7 VV JERS-1 SAR 1992 L 23.5 HH ERS-2 AMI 1995 C 5.7 VV Radarsat-1 SAR 1995 C 5.7 HH Envisat ASAR 2002 C 5.7 HH/VV Alos PALSAR 2006 L 23.5 All a Radarsat-2 SAR 2007 C 5.7 All TerraSAR-X TSX X 3 All Cosmo/SkyMe d 1, 2, 3, 4 SAR , 2007, 2008, 2010 X 3 HH/VV TerraSAR-L SAR 2008 L 23.5 All (plan) TanDEM-X b TSX-SAR 2010 X 3 HH/VV [Zhang and Kerle, 2007; Düring et al., 2008] 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 6
7 Airborne LiDAR sensors Brand Name Date of update Weight [kg] Wavelength [nm] Elevation precision at 1km [cm] Overall planimetric precision [cm] Max. # of points/m 2 Airborne Hydrography AB Leica Geosystems Optech RIEGL TopoSys Dragon kg 1,000 GPS/INS GPS/INS Pending 150m, 300kHz, 20m/s Eye Pending Hawk Eye II 2006/ kg 532 /1,064 Bathy<50 Topo<30 Bathy<5m Topo<1m Bathy 1/m 2, topo 10/m 2 ALS kg 1, km/h, 200m, 15 ALTM Gemini ALTM Orion RIEGL VQ-480 RIEGL LMS-Q560 RIEGL LMS-Q680 Harrier 56/G kg 1,064 < 10 1/ kg 1,064 < 10 1/ kg 1,550 < 15 < 10 50km/h, 150m, kg 1,550 < 15 < km/h, 500m, 60 50km/h, 150m, kg 1,550 < 15 < km/h, 500m, kg 1,550 < 15 < km/h, 500 m, 60 50km/h, 150m, 60 Harrier 2009 N/A 1,550 < 15 < km/h, 500m, 60 68/G1 Falcon II 2000 / kg 1,560 < 15 < km/h, 500m, 14.3 [GIM International, 2009] 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 7
8 2. AGIP Lab Technologies 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 8
9 2.a. Image Fusion 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 9
10 Popular IHS Technique for IKONOS Fusion IKONOS 4m Multispectral IHS Fused 1m IKONOS Image + = Significant Colour Distortion IKONOS 1m Panchromatic 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 10
11 QuickBird 60-cm natural color image ( Brussels, Belgium, June 2, /8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 11
12 IKONOS UNB Fusion (UNB PanSharp) IKONOS 4m Multispectral New 1 mikonos Fusion Image + = Minimized Colour Distortion IKONOS 1m Panchromatic 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 12
13 Landsat 7 ETM+ Image Fusion (Bands 1, 2 and 3) 30m Multispectral 15m Panchromatic 15m UNB Fusion Result 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 13
14 Landsat 7 ETM+ Image Fusion (Bands 2, 3 and 4) 30m Multispectral 15m Panchromatic 15m UNB Fusion Result 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 14
15 QuickBird 2.8m MS Courtesy Digital Globe 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 15
16 QuickBird 0.7m Pan Courtesy Digital Globe 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 16
17 QuickBird 0.7m MS, UNB Fusion Courtesy Digital Globe 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 17
18 QuickBird 0.7m MS, UNB Fusion & Color Enhancement Courtesy Digital Globe 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 18
19 QuickBird 2.4m MS Courtesy Digital Globe 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 19
20 QuickBird 0.6m MS, UNB Fusion Courtesy Digital Globe 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 20
21 GeoEye-1, MS 1, 2 and 3, 2m CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
22 GeoEye-1, Pan, 0.5m CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
23 GeoEye-1, UNB-PanSharp, 0.5m CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
24 GeoEye-1, GeoEye-Pansharp, 0.5m CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
25 GeoEye-1, MS 1, 2 and 3, 2m CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
26 GeoEye-1, Pan, 0.5m CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
27 GeoEye-1, UNB-PanSharp, 0.5m CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
28 GeoEye-1, GeoEye-Pansharp, 0.5m CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
29 GeoEye-1, MS 1, 2 and 3, 2m CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
30 GeoEye-1, Pan, 0.5m CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
31 GeoEye-1, UNB-PanSharp, 0.5m CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
32 GeoEye-1, GeoEye-Pansharp, 0.5m CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
33 Conclusions UNB Fusion UNB-Pansharp (1) Fully automated, one step process. (2) All the fusions have shown a perfect result with: maximum detail increasing, minimum colour distortion, and natural colour and feature integration. 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 33
34 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 34
35 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 35
36 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 36
37 GigitalGlobe s employees waiting for QuickBird, Boulder, CO Photography: Yun Zhang, 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 37
38 GigitalGlobe s employees thank QuickBird image users, Boulder, CO Photography: QuickBird satellite, Oct 10, /8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 38
39 Dr. Y. Zhang helping DG install UNB-Pansparp 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 39
40 UNB-Pansharp Before using UNB-Pansharp 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 40
41 Being used worldwide, including NASA, Google, and US and Canadian national security. One of 9 Canadian successful research achievements for the "Technology Transfer Works: 100 Cases from Research to Realization", by the Association of University Technology Managers Other universities being selected into the 100 Cases include: MIT, Yale, Stanford, Columbia and Brown Universities. 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 41
42 2.b. Adjustable SAR-MS Fusion UNB-ASMF 5/14/2009 Canada Research Chair Laboratary in Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 42
43 Adjustable SAR-MS Fusion Radarsat, SAR (8m) Landsat TM, MS (30m) For display purpose, the same standard linear image stretching is applied to all the images. Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 43
44 Original Landsat, MS 123 (30m) Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 44
45 Original Radarsat, SAR (8m) Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 45
46 UNB Adjustable SAR-MS Fusion, Level 1 Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 46
47 UNB Adjustable SAR-MS Fusion, Level 2 Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 47
48 Original Landsat, MS 234 (30m) Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 48
49 Original Radarsat, SAR (8m) Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 49
50 UNB Adjustable SAR-MS Fusion, Level 1 Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 50
51 UNB Adjustable SAR-MS Fusion, Level 2 Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 51
52 Original Landsat, MS 345 (30m) Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 52
53 Original Radarsat, SAR (8m) Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 53
54 UNB Adjustable SAR-MS Fusion, Level 1 Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 54
55 UNB Adjustable SAR-MS Fusion, Level 2 Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 55
56 Adjustable SAR-MS Fusion Radarsat, SAR (12.5m) Ikonos, MS (4m) For display purpose, the same standard linear image stretching is applied to all the images. Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 56
57 Original Ikonos, MS (4m) (No image stretching is applied) Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 57
58 Original Radarsat, SAR (12.5m) (No image stretching is applied) Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 58
59 UNB Adjustable SAR-MS Fusion, Level 1 (No image stretching is applied) Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 59
60 UNB Adjustable SAR-MS Fusion, Level 2 (No image stretching is applied) Canada Research Chair Laboratary in 5/14/2009 Advanced Geomatics Image Processing, CRC-Laboratory in Advanced UNB, Geomatics Canada Image Processing, UNB, Canada 60
61 2.c. Moving Target Detection 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 61
62 QuickBird Pan 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 62
63 QuickBird MS 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 63
64 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 64
65 QuickBird Pan-MS arrays alignment Reference: Padwick, C., Pan Sharpening of High Resolution Satellite Imagery, ASPRS Annual Conference, June 8, /8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 65
66 Moving targets /8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 66
67 Moving targets /8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 67
68 Table 5: Coordinates, speed, and azimuth angle of moving targets Pan No X(m) Y(m) H(m) Speed(km/h) Azimuth(degree) On slow lane On road side On high speed lane, will pass over 13 Big truck Will pass over 17 Just passed over 15 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 68
69 Speed and direction of moving target: 118 km/h km/h 109 km/h km/h 68 km/h 133 km/h /8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 69
70 Speed and direction of moving target: ASPRS John I. Davidson President s Award for Practical Paper, 2009, with my PhD student Z. Xiong /8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 70
71 2.d. Image Matching 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 71
72 Problems with the Existing Solutions Ambiguity in smooth (low texture) areas, such as forest, grass, water, highway surfaces, building roofs, etc. A A C B D 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 72
73 UNB Image Matching 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 73
74 UNB Image Matching Right image rotates /8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 74
75 UNB Image Matching 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 75
76 2.e. Image Segmentation 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 76
77 Problem in object-oriented classification Segmentation in ecognition TM The operator must use his/her experience and a trial-and-error method to find the appropriate segmentation parameters: Scale =? Shape weight (factor) =? Smoothness =? 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
78 Existing object-oriented classification (ecognition) Step 1: Segmentation at various scales Step 2: Classification of image objects 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
79 UNB Supervised Segmentation (1) Initial Segmentation Perform Preliminary Segmentation Parameters used: Scale = 25 Shape weight = 0.1 Smoothness = 0.1 weight 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
80 UNB Supervised Segmentation (2) Segmentation Training Train the system by selecting appropriate subobjects that comprise the object of interest Start iterative process to determine appropriate segmentation parameters 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
81 UNB Supervised Segmentation (3) Automatically finding optimal segmentation parameters Convergence in 4 iterations Solution parameters: Scale = 120 Shape weight = Smoothness = weight 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
82 Re-segmentation Results and Comparison Trial and error approach (State-of-the-art) UNB approach (UNB solution) 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 82
83 Re-segmentation Results and Comparison Trial and error approach UNB approach 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 83
84 Re-segmentation Results (UNB result) 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 84
85 UNB Supervised Segmentation ecogintion UNB SP Optimizer 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 85
86 2.f. InSAR DEM Reconstruction 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 86
87 Interferometric SAR (InSAR) for DEM generation CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
88 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
89 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
90 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
91 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
92 - ASPRS Talbert Abrams Grand Award - Used by Intermap Technologies Inc. CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
93 2.g. Online 3D 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 93
94 Developed in /8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 94
95 University of New Brunswick < CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
96 Glasses-free 2D and 3D monitors by Sharp, Philips, Toshiba CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
97 3D display in Kunming airport, China CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
98 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
99 Web-I-3D, 3D satellite image CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
100 Web-I-3D, 3D satellite image CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
101 Web-I-3D, 3D satellite image (University of New Brunswick, Canada) CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
102 Web-I-3D, 3D satellite image Fredericton, Canada University of New Brunswick < CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
103 3D Visualization CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
104 2D Visualization CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
105 3D Visualization CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
106 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 106
107 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 107
108 2.h. Generic RPC sensor model refinement 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 108
109 RMSE(pixels) Accuracy comparison between the Bias method and Generic RPC Refinement Method Col. RMSE-Generic Row RMSE-Generic Col. RMSE-Bias Row RMSE-Bias Figure 7: Accuracy comparison between the Bias Compensation method and the Generic RPC Refinement Method developed in CRC-AGIP using Ikonos images (narrow field of view) in 3 cases (all with small sensor position and attitude errors) (Note: RMSE = Root Mean Square Error; Row = Row direction of image; Col. = Column direction of image; Generic = Generic RPC Refinement Method; Bias = Bias Compensation method). 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 109
110 Accuracy comparison between the Bias method and Generic RPC Refinement Method RMSE(pixels) 1 GCP, 36 CHKs Case 3: Sensor position error is 1000m in x, y, z, and attitude error is 0.1 radian about the three axes Col. RMSE-Generic Row RMSE-Generic Col. RMSE-Bias Row RMSE-Bias Case 9: Sensor position error is 0, and attitude error is 0.1 radian Figure 8: Accuracy comparison between the Bias Compensation method and Generic RPC Refinement Method using simulated SPOT-5 data with 9 different magnitudes of errors and using 1 GCP as ground control and 36 check points for accuracy assessment. (Case 2: the sensor position error is 100m in x, y and z directions, and the sensor attitude error is 0.01 radian about the three axes; Case 3: position error is 1000m in x, y, z, and attitude error is 0.1 radian about the three axes; Case 8: position error is 0, and attitude error is 0.01 radian; Case 9: position error is 0, and attitude error is 0.1 radian; and Cases 1, 4, 5, 6, and 7: the sensor position error varies from 10m to 1000m in x, y, z, and attitude error varies from 0.0 to radian.) 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 110
111 Accuracy comparison between the Bias method and Generic RPC Refinement Method RMSE(pixels) 3 GCPs, 34 CHKs 8 6 Case 3: Sensor position error is 1000m in x, y, z, and attitude error is 0.1 radian about the three axes Col. RMSE-Generic Row RMSE-Generic Col. RMSE-Bias Row RMSE-Bias Case 9: Sensor position error is 0, and attitude error is 0.1 radian Figure 9: Accuracy comparison between the Bias Compensation method and Generic RPC Refinement Method using simulated SPOT-5 data with 9 different magnitudes of errors and using 3 GCP as ground control and 34 check points for accuracy assessment. (The magnitudes of errors of the 9 cases are the same as in Figure 8.) 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 111
112 Technology transfer and commercialization: 1)Image fusion (UNB-PanSharp), to PCI Geomatics (2002), resulting in the best image fusion software and industry standard, used by end users globally, developer: Y. Zhang. 2)Image fusion technology, to DigitalGlobe (2003), for producing all pansharpened QuickBird imagery and now WorldView-2 imagery for worldwide distribution, developer: Y. Zhang. 3)Radar image colourization technology, to Intermap Technology (2005), for value added production of SAR images, developers: G. Hong (my PhD student) and Y. Zhang. 4)Colour enhancement technique, to PCI Geomatics (2006), developer: Y. Zhang. 5)Adjustable SAR image colourization, to PCI Geomatics (2008), developer: Y. Zhang. 6)Image fusion technology, to a US company, for security and intelligence (2008), Y. Zhang. 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 112
113 Patents: (1) US Patent (7,379,590): Method for Generating Natural Colour Satellite Images, 2008, Zhang. (2) US Patent (7,340,099): System and method for image fusion, 2008, Zhang. (3) Canadian Patent (2,491,794):Method for Generating Natural Colour Satellite Images, Zhang. (4) US Patent Application (11/656,950): Method of Image Segmentation, 2007, with student Maxwell. (5) US Patent Application (12/775,240): Method of Interest Point Matching for Images, 2010, with student Xiong. (6) Canadian Patent Application (61/175,934): Method of Interest Point Matching for Images, 2010, with student Xiong. (7) US Patent Application (12/775,259): Method for RPC Refinement Using Ground Control Information, 2010, with student Xiong. (8) Canadian Patent Application (61/175,944): Method for RPC Refinement Using Ground Control Information, 2010, with student Xiong. (9) US Patent Application: Dual Video Camera System and Method, 2010, Zhang. 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 113
114 CRC-AGIP (CRC-Laboratory in Advanced Geomatics Image Processing) (Azienda Generale Italiana Petroli) established in CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada
115 Thank you! 11/8/2010 CRC-Laboratory in Advanced Geomatics Image Processing, UNB, Canada 115
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