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7 Table des matières 1 Introduction 11 2 Remote Sensing with MODIS 15 2.1 The evolution of remote sensing . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2 Applications of satellite imagery . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.1 Monitoring land changes . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.2 Studying the atmosphere dynamics . . . . . . . . . . . . . . . . . . . 17 2.2.3 Cryosphere and climate change . . . . . . . . . . . . . . . . . . . . . 19 2.2.4 Recent exemples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.4.1 The Eyjafjallajökull . . . . . . . . . . . . . . . . . . . . . . 20 2.2.4.2 Deepwater Horizon oil spill . . . . . . . . . . . . . . . . . . 20 2.3 The importance of oceans in the Earth system . . . . . . . . . . . . . . . . 21 2.3.1 The ocean’s carbon cyle . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.2 Phytoplancton and ocean color . . . . . . . . . . . . . . . . . . . . . 22 2.4 Constraints in remote sensing . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.4.1 Sensor Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.4.2 Atmospheric correction . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.4.3 Sun glint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.4.4 Cloud coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.5 The Moderate Resolution Imaging Spectroradiometer (MODIS) . . . . . . . 28 2.5.1 Context and objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.5.2 Technical specifications . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.3 Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.5.4 MODIS products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.5.5 Stripe noise on MODIS . . . . . . . . . . . . . . . . . . . . . . . . . 33 3 Standard destriping techniques and application to MODIS 39 3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.2 Moment Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3 Histogram Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 3.4 Overlapping Field-of-View Method . . . . . . . . . . . . . . . . . . . . . . . 48
Page 1: École Doctorale d’Informatique,
Page 5: 5 Résumé Nou abordons dans cette
Page 9 and 10: 9 5.3.1.3 Euclidian Distance Measur
Page 11 and 12: 11 Chapitre 1 Introduction Over the
Page 13 and 14: 13 results of our work using data f
Page 15 and 16: 15 Chapitre 2 Remote Sensing with M
Page 17 and 18: 17 Figure 2.1 - Deforestation in Br
Page 19 and 20: 19 Figure 2.3 - Monitoring snow cov
Page 21 and 22: 21 Figure 2.5 - RGB images from MOD
Page 23 and 24: 23 radiance and other mineral compo
Page 25 and 26: 25 coincide with those derived from
Page 27 and 28: 27 POLYMER based on neural networks
Page 29 and 30: 29 morning and afternoon observatio
Page 31 and 32: 31 Figure 2.10 - Main Components of
Page 33 and 34: 33 Blackbody : The calibration of M
Page 35 and 36: 35 ration of level 2 and level 3 pr
Page 37 and 38: 37 Discipline Product ID Product na
Page 39 and 40: 39 Chapitre 3 Standard destriping t
Page 41 and 42: 41 for striping. The drift observed
Page 43 and 44: Figure 3.2 - Sub-images acquired by
Page 45 and 46: 45 magnitude. The derived technique
Page 47 and 48: 47 ECDF 1 0.9 0.8 0.7 0.6 0.5 0.4 0
Page 49 and 50: 49 Figure 3.7 - (Left) Bowtie effec
Page 51 and 52: 51 Figure 3.8 - (Left) Noisy image
Page 53 and 54: 53 12 11 10 10 Power spectrum 8 6 4
Page 55 and 56: 55 Image distortion ID 1 0.98 0.96
Page 57 and 58:
57 Figure 3.14 - (Left) Image from
Page 59 and 60:
59 function ψ, the mother wavelet.
Page 61 and 62:
61 Figure 3.15 - (Left) Noisy image
Page 63 and 64:
63 The highest resolution is used f
Page 65 and 66:
65 sholding) can be tuned to regula
Page 67 and 68:
67 A secondary index, independent o
Page 69 and 70:
Figure 3.18 - Destriped results on
Page 71 and 72:
71 10 10 9 9 Power spectrum 8 7 6 5
Page 73 and 74:
73 Chapitre 4 A Variational approac
Page 75 and 76:
Page 77 and 78:
77 The goal is to find the true ima
Page 79 and 80:
79 Fast and exact minimization algo
Page 81 and 82:
81 Using Bayes’rule the previous
Page 83 and 84:
83 Meyer’s work can then be used
Page 85 and 86:
85 coupled Euler-Lagrange equations
Page 87 and 88:
87 The minimization of (4.60) is ob
Page 89 and 90:
Figure 4.4 - Decomposition of the i
Page 91 and 92:
91 4.4 Destriping via gradient fiel
Page 93 and 94:
93 Figure 4.6 - (Left) Horizontal a
Page 95 and 96:
Page 97 and 98:
97 Using the same notations as in s
Page 99 and 100:
99 which is not unique. Indeed, the
Page 101 and 102:
101 Figure 4.13 - From left to righ
Page 103 and 104:
103 Image distortion 1 0.95 0.9 0.8
Page 105 and 106:
105 17500 23500 Mean value 16500 15
Page 107 and 108:
Table 4.1 - Noise Reduction (NR), I
Page 109 and 110:
Figure 4.18 - Destriping results wi
Page 111 and 112:
111 Chapitre 5 Application : Restor
Page 113 and 114:
113 Figure 5.2 - Location of the Te
Page 115 and 116:
115 ρ 6 (x max ) MODIS Band 6 ρ 6
Page 117 and 118:
117 pixels can then be restored wit
Page 119 and 120:
119 Figure 5.5 - Restoration of Ter
Page 121 and 122:
e square or rectangular depending o
Page 123 and 124:
Figure 5.9 - (TL) Original image fr
Page 125 and 126:
125 40 1 40 1 35 30 0.99 35 30 0.99
Page 127 and 128:
127 Conclusion Contribution Perspec
Page 129 and 130:
129 Bibliographie R. Acar and C. R.
Page 131 and 132:
131 A. Efros and T. Leung. Texture
Page 133 and 134:
133 C. M. E. and W. W. Gregg. Compa
Page 135:
135 W. M. Strome and S. S. Vishnubh
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