diff --git a/SoftwareGuide/Art/FuzzyConnectednessClassDiagram1.fig b/SoftwareGuide/Art/FuzzyConnectednessClassDiagram1.fig
new file mode 100755
index 0000000000000000000000000000000000000000..f5e72321d51c509daa8700122172793e3e1c91eb
--- /dev/null
+++ b/SoftwareGuide/Art/FuzzyConnectednessClassDiagram1.fig
@@ -0,0 +1,38 @@
+#FIG 3.2
+Landscape
+Center
+Inches
+Letter
+100.00
+Single
+-2
+1200 2
+6 4650 900 7425 1350
+2 2 0 1 0 7 50 0 -1 0.000 0 0 -1 0 0 5
+ 4650 900 7425 900 7425 1350 4650 1350 4650 900
+4 1 0 50 0 12 12 0.0000 4 180 2415 6000 1200 itk::ImageToImageFilter\001
+-6
+6 3375 1875 8325 2325
+2 2 0 1 0 7 50 0 -1 0.000 0 0 -1 0 0 5
+ 3375 1875 8325 1875 8325 2325 3375 2325 3375 1875
+4 1 0 50 0 12 12 0.0000 4 180 4620 5850 2175 itk::SimpleFuzzyConnectednessImageFilterBase\001
+-6
+6 525 2925 5775 3375
+2 2 0 1 0 7 50 0 -1 0.000 0 0 -1 0 0 5
+ 525 2925 5775 2925 5775 3375 525 3375 525 2925
+4 1 0 50 0 12 12 0.0000 4 180 4830 3150 3225 itk::SimpleFuzzyConnectednessScalarImageFilter\001
+-6
+6 6225 2925 11175 3375
+2 2 0 1 0 7 50 0 -1 0.000 0 0 -1 0 0 5
+ 6225 2925 11175 2925 11175 3375 6225 3375 6225 2925
+4 1 0 50 0 12 12 0.0000 4 180 4515 8700 3225 itk::SimpleFuzzyConnectednessRGBImageFilter\001
+-6
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 0 1 2
+ 0 0 1.00 60.00 120.00
+ 6000 1350 6000 1875
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 0 1 2
+ 0 0 1.00 60.00 120.00
+ 7200 2325 7200 2925
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 0 1 2
+ 0 0 1.00 60.00 120.00
+ 4350 2325 4350 2925
diff --git a/SoftwareGuide/Art/FuzzyConnectednessCollaborationDiagram1.fig b/SoftwareGuide/Art/FuzzyConnectednessCollaborationDiagram1.fig
new file mode 100755
index 0000000000000000000000000000000000000000..ed6d712f98ce58cfc39c2eec5b1ef92cc80494da
--- /dev/null
+++ b/SoftwareGuide/Art/FuzzyConnectednessCollaborationDiagram1.fig
@@ -0,0 +1,49 @@
+#FIG 3.2
+Landscape
+Center
+Inches
+Letter
+100.00
+Single
+-2
+1200 2
+6 1500 3225 2550 4125
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 2025 3675 525 450 2025 3675 2550 4125
+4 1 0 50 0 0 12 0.0000 0 180 390 2025 3600 Input\001
+4 1 0 50 0 0 12 0.0000 0 180 750 2025 3825 itk::Image\001
+-6
+6 8100 3225 9150 4125
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 8625 3675 525 450 8625 3675 9150 4125
+4 1 0 50 0 0 12 0.0000 0 180 525 8625 3600 Output\001
+4 1 0 50 0 0 12 0.0000 0 180 750 8625 3825 itk::Image\001
+-6
+6 5400 2175 7200 2775
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 6300 2475 900 300 6300 2475 7200 2775
+4 1 0 50 0 0 12 0.0000 0 135 1515 6300 2550 Estimated Variance\001
+-6
+6 3450 2175 5250 2775
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 4350 2475 900 300 4350 2475 5250 2775
+4 1 0 50 0 0 12 0.0000 0 135 1260 4350 2550 Estimated Mean\001
+-6
+6 3450 4575 5250 5175
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 4350 4875 900 300 4350 4875 5250 5175
+4 1 0 50 0 0 12 0.0000 0 135 1350 4350 4950 Other Parameters\001
+-6
+2 4 0 1 0 7 50 0 -1 0.000 0 0 7 0 0 5
+ 7275 4125 7275 3225 3375 3225 3375 4125 7275 4125
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 2550 3675 3375 3675
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 7275 3675 8100 3675
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 4350 2775 4350 3225
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 6300 2775 6300 3225
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 4350 4575 4350 4125
+4 1 0 50 0 12 12 0.0000 4 180 3045 5325 3750 FuzzyConnectednessImageFilter\001
diff --git a/SoftwareGuide/Art/FuzzyVoronoiCollaborationDiagram1.fig b/SoftwareGuide/Art/FuzzyVoronoiCollaborationDiagram1.fig
new file mode 100755
index 0000000000000000000000000000000000000000..15547398c476aafd475d93a9efc98b5e44f2c7c7
--- /dev/null
+++ b/SoftwareGuide/Art/FuzzyVoronoiCollaborationDiagram1.fig
@@ -0,0 +1,84 @@
+#FIG 3.2
+Landscape
+Center
+Inches
+Letter
+100.00
+Single
+-2
+1200 2
+6 3750 1350 5550 1950
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 4650 1650 900 300 4650 1650 5550 1950
+4 1 0 50 0 0 12 0.0000 0 135 1515 4650 1725 Estimated Variance\001
+-6
+6 1800 1350 3600 1950
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 2700 1650 900 300 2700 1650 3600 1950
+4 1 0 50 0 0 12 0.0000 0 135 1260 2700 1725 Estimated Mean\001
+-6
+6 1725 2400 5625 3300
+2 4 0 1 0 7 50 0 -1 0.000 0 0 7 0 0 5
+ 5625 3300 5625 2400 1725 2400 1725 3300 5625 3300
+4 1 0 50 0 0 12 0.0000 0 180 2505 3675 2925 FuzzyConnectednessImageFilter\001
+-6
+6 150 2400 1200 3300
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 675 2850 525 450 675 2850 1200 3300
+4 1 0 50 0 0 12 0.0000 0 180 390 675 2775 Input\001
+4 1 0 50 0 0 12 0.0000 0 180 750 675 3000 itk::Image\001
+-6
+6 7575 2400 11475 3300
+2 4 0 1 0 7 50 0 -1 0.000 0 0 7 0 0 5
+ 11475 3300 11475 2400 7575 2400 7575 3300 11475 3300
+4 1 0 50 0 0 12 0.0000 0 180 2505 9525 2925 VoronoiSegmentationImageFilter\001
+-6
+6 6075 2400 7125 3300
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 6600 2850 525 450 6600 2850 7125 3300
+4 1 0 50 0 0 12 0.0000 0 180 495 6600 2775 Binary\001
+4 1 0 50 0 0 12 0.0000 0 180 750 6600 3000 itk::Image\001
+-6
+6 3750 3750 5550 4350
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 4650 4050 900 300 4650 4050 5550 4350
+4 1 0 50 0 0 12 0.0000 0 135 1350 4650 4125 Other Parameters\001
+-6
+6 11925 2400 12975 3300
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 12450 2850 525 450 12450 2850 12975 3300
+4 1 0 50 0 0 12 0.0000 0 180 525 12450 2775 Output\001
+4 1 0 50 0 0 12 0.0000 0 180 750 12450 3000 itk::Image\001
+-6
+6 7725 1350 9525 1950
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 8625 1650 900 300 8625 1650 9525 1950
+4 1 0 50 0 0 12 0.0000 0 135 1230 8625 1725 Mean Tolerance\001
+-6
+6 9675 1350 11475 1950
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 10575 1650 900 300 10575 1650 11475 1950
+4 1 0 50 0 0 12 0.0000 0 135 1140 10575 1725 STD Tolerance\001
+-6
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 1200 2850 1725 2850
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 5625 2850 6075 2850
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 2700 1950 2700 2400
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 4650 1950 4650 2400
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 7125 2850 7575 2850
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 4
+ 0 0 1.00 60.00 120.00
+ 1425 2850 1425 4650 9525 4650 9525 3300
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 4650 3750 4650 3300
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 11475 2850 11925 2850
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 10650 1950 10650 2400
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 8625 1950 8625 2400
diff --git a/SoftwareGuide/Art/FuzzyVoronoiDeformableCollaborationDiagram1.fig b/SoftwareGuide/Art/FuzzyVoronoiDeformableCollaborationDiagram1.fig
new file mode 100755
index 0000000000000000000000000000000000000000..2c7ec51ac512e772920c45ee49a18a511af1d713
--- /dev/null
+++ b/SoftwareGuide/Art/FuzzyVoronoiDeformableCollaborationDiagram1.fig
@@ -0,0 +1,49 @@
+#FIG 3.2
+Landscape
+Center
+Inches
+Letter
+100.00
+Single
+-2
+1200 2
+6 150 2400 1200 3300
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 675 2850 525 450 675 2850 1200 3300
+4 1 0 50 0 0 12 0.0000 0 180 390 675 2775 Input\001
+4 1 0 50 0 0 12 0.0000 0 180 750 675 3000 itk::Image\001
+-6
+6 6075 2400 7125 3300
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 6600 2850 525 450 6600 2850 7125 3300
+4 1 0 50 0 0 12 0.0000 0 180 495 6600 2775 Binary\001
+4 1 0 50 0 0 12 0.0000 0 180 750 6600 3000 itk::Image\001
+-6
+6 1725 2400 5625 3300
+2 4 0 1 0 7 50 0 -1 0.000 0 0 7 0 0 5
+ 5625 3300 5625 2400 1725 2400 1725 3300 5625 3300
+4 1 0 50 0 0 12 0.0000 0 180 3465 3675 2925 FuzzyConnectedness + VoronoiSegmentation\001
+-6
+6 7575 2400 9750 3300
+2 4 0 1 0 7 50 0 -1 0.000 0 0 7 0 0 5
+ 9750 3300 9750 2400 7575 2400 7575 3300 9750 3300
+4 1 0 50 0 0 12 0.0000 0 135 1410 8700 2925 Deformable Model\001
+-6
+6 10200 2400 11250 3300
+1 1 0 1 0 7 50 0 -1 0.000 1 0.0000 10725 2850 525 450 10725 2850 11250 3300
+4 1 0 50 0 0 12 0.0000 0 180 525 10725 2775 Output\001
+4 1 0 50 0 0 12 0.0000 0 180 750 10725 3000 itk::Image\001
+-6
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 1200 2850 1725 2850
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 5625 2850 6075 2850
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 7125 2850 7575 2850
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 9750 2850 10200 2850
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 4
+ 0 0 1.00 60.00 120.00
+ 1425 2850 1425 3825 8625 3825 8625 3300
diff --git a/SoftwareGuide/Art/HybridSegmentationEngine1.fig b/SoftwareGuide/Art/HybridSegmentationEngine1.fig
new file mode 100755
index 0000000000000000000000000000000000000000..3b36c8cbb3e177c88d99b74bb8a52c24b3fc7067
--- /dev/null
+++ b/SoftwareGuide/Art/HybridSegmentationEngine1.fig
@@ -0,0 +1,84 @@
+#FIG 3.2
+Landscape
+Center
+Inches
+Letter
+100.00
+Single
+-2
+1200 2
+6 4875 2775 6900 3450
+2 2 0 1 0 7 50 0 -1 0.000 0 0 -1 0 0 5
+ 4875 2775 6900 2775 6900 3450 4875 3450 4875 2775
+4 1 0 50 0 0 12 0.0000 0 135 885 6000 3075 Deformable\001
+4 1 0 50 0 0 12 0.0000 0 180 975 6000 3300 Model (DM)\001
+-6
+6 4875 3825 6900 4500
+2 2 0 1 0 7 50 0 -1 0.000 0 0 -1 0 0 5
+ 4875 3825 6900 3825 6900 4500 4875 4500 4875 3825
+4 1 0 50 0 0 12 0.0000 0 135 1485 5925 4125 Gibb Prior / Markov\001
+4 1 0 50 0 0 12 0.0000 0 180 1620 5925 4350 Random Field (MRF)\001
+-6
+6 4875 4800 6900 5400
+2 2 0 1 0 7 50 0 -1 0.000 0 0 -1 0 0 5
+ 4875 4800 6900 4800 6900 5400 4875 5400 4875 4800
+4 1 0 50 0 0 12 0.0000 0 135 600 5925 5025 Voronoi\001
+4 1 0 50 0 0 12 0.0000 0 180 645 5925 5250 Diagram\001
+-6
+6 4875 5700 6900 6300
+2 2 0 1 0 7 50 0 -1 0.000 0 0 -1 0 0 5
+ 4875 5700 6900 5700 6900 6300 4875 6300 4875 5700
+4 1 0 50 0 0 12 0.0000 0 180 465 5925 5925 Fuzzy\001
+4 1 0 50 0 0 12 0.0000 0 180 1590 5925 6150 Connectedness (FC)\001
+-6
+6 2100 4275 2850 4650
+4 1 0 50 0 0 12 0.0000 0 135 705 2475 4425 Vectorial\001
+4 1 0 50 0 0 12 0.0000 0 135 465 2475 4650 Scene\001
+-6
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 0 1 2
+ 0 0 1.00 60.00 120.00
+ 5625 3450 5625 3825
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 0 1 2
+ 0 0 1.00 60.00 120.00
+ 6150 3825 6150 3450
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 5625 5700 5625 5400
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 6150 5400 6150 5700
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 4
+ 0 0 1.00 60.00 120.00
+ 6900 3225 7425 3225 7425 5925 6900 5925
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 6900 3075 7875 3075
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 6900 4200 7875 4200
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 6900 5100 7875 5100
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 6900 6075 7875 6075
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 4
+ 0 0 1.00 60.00 120.00
+ 4875 5925 4350 5925 4350 3225 4875 3225
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 1 4
+ 0 0 1.00 60.00 120.00
+ 0 0 1.00 60.00 120.00
+ 4875 3000 3825 3000 3825 6075 4875 6075
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 3825 4125 4875 4125
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 3825 5025 4875 5025
+2 1 0 1 0 7 50 0 -1 0.000 0 0 -1 1 0 2
+ 0 0 1.00 60.00 120.00
+ 3000 4575 3825 4575
+4 0 0 50 0 0 12 0.0000 0 180 1065 8025 3150 Binary Output\001
+4 0 0 50 0 0 12 0.0000 0 180 1065 8025 4275 Binary Output\001
+4 0 0 50 0 0 12 0.0000 0 180 1065 8025 5175 Binary Output\001
+4 0 0 50 0 0 12 0.0000 0 180 1065 8025 6150 Binary Output\001
diff --git a/SoftwareGuide/Examples/CMakeLists.txt b/SoftwareGuide/Examples/CMakeLists.txt
index 08f37e551834535dac15e9211b5f2fbb9d12bd87..a0fdb2bba928f6858e0a7b2dbaa055476258b810 100644
--- a/SoftwareGuide/Examples/CMakeLists.txt
+++ b/SoftwareGuide/Examples/CMakeLists.txt
@@ -202,7 +202,7 @@ SET( OTB_EXAMPLES_SRCS
${OTB_SOURCE_DIR}/Examples/Segmentation/IsolatedConnectedImageFilter.cxx
${OTB_SOURCE_DIR}/Examples/Segmentation/WatershedSegmentation.cxx
${OTB_SOURCE_DIR}/Examples/Segmentation/FastMarchingImageFilter.cxx
- ${OTB_SOURCE_DIR}/Examples/Patented/HybridSegmentationFuzzyVoronoi.cxx
+ #${OTB_SOURCE_DIR}/Examples/Patented/HybridSegmentationFuzzyVoronoi.cxx
${OTB_SOURCE_DIR}/Examples/Patented/FuzzyConnectednessImageFilter.cxx
${OTB_SOURCE_DIR}/Examples/ChangeDetection/ChangeDetectionFrameworkExample.cxx
${OTB_SOURCE_DIR}/Examples/ChangeDetection/DiffChDet.cxx
diff --git a/SoftwareGuide/Latex/ChangeDetection.tex b/SoftwareGuide/Latex/ChangeDetection.tex
index a4e6324623989d8c217fe28e2dccac0384f2e2f7..1f7e850b2539b71edf8e4a1c8d14552ebd38a834 100644
--- a/SoftwareGuide/Latex/ChangeDetection.tex
+++ b/SoftwareGuide/Latex/ChangeDetection.tex
@@ -1,6 +1,151 @@
\chapter{Change Detection}
\section{Introduction}
-Similarity, windows, 2 dates, etc.
+Change detection techniques try to detect and locate areas which have
+changed between two or more observations of the same scene. These
+changes can be of different types, with different origins and of
+different temporal length. This allows to distinguish different kinds
+of applications:
+\begin{itemize}
+\item \emph{land use monitoring}, which corresponds to the
+ characterization of the evolution of the vegetation, or its seasonal
+ changes;
+\item \emph{natural resources management}, which corresponds mainly
+ to the characterisation of the evolution of the urban areas, the
+ evolution of the deforestation, etc.
+\item \emph{damage mapping}, which corresponds to the location of
+ damages caused by natural or industrial disasters.
+\end{itemize}
+
+From the point of view of the observed phenomena, one can distinguish
+2 types of changes whose nature is rather different: the abrupt
+changes and the progressive changes, which can eventually be
+periodic. From the data point of view, one can have:
+
+ \begin{itemize}
+ \item Image pairs before and after the event. The applications
+ are mainly the abrupt changes.
+
+ \item Multi-temporal image series on which 2 types on changes
+ may appear:
+ \begin{itemize}
+ \item The slow changes like for instance the erosion,
+ vegetation evolution, etc. The knowledge of the studied
+ phenomena and of their consequences on the geometrical
+ and radiometrical evolution at the different dates is a
+ very important information for this kind of analysis.
+
+ \item The abrupt changes may pose different kinds of
+ problems depending on whether the date of the change is
+ known in the image series or not. The detection of areas
+ affected by a change occurred at a known date may exploit
+ this a priori information in order to split the image
+ series into two sub-series (before an after) and use the
+ temporal redundancy in order to improve the detection
+ results. On the other hand, when the date of the change
+ is not known, the problem has a higher difficulty.
+
+ \end{itemize}
+
+ \end{itemize}
+
+From this classification of the different types of problems, one can
+infer 4 cases for which one can look for algorithms as a function of
+the available data:
+\begin{enumerate}
+\item Abrupt changes in an image pair. This is no doubt the field for
+ which more work has been done. One can find tools at the 3 classical
+ levels of image processing: data level (differences, rations, with
+ or without pre-filtering, etc.), feature level (edges, targets,
+ etc.), and interpretation level (post-classification comparison).
+
+\item Abrupt changes within an image series and a known date. One can
+ rely on bi-date techniques, either by fusing the images into 2 stacks
+ (before and after), or by fusing the results obtained by different
+ image couples (one after and one before the event). One can also use
+ specific discontinuity detection techniques to be applied in the
+ temporal axis.
+
+\item Abrupt changes within an image series and an unknown date. This
+ case can be seen either as a generalization of the preceding one (testing
+ the N-1 positions for N dates) or as a particular case of the
+ following one.
+
+\item Progressive changes within an image series. One can work in two
+ steps:
+ \begin{enumerate}
+ \item detect the change areas using stability criteria in the
+ temporal areas;
+ \item identify the changes using prior information about the type
+ of changes of interest.
+ \end{enumerate}
+
+\end{enumerate}
+
+
+
+
+\subsection{Surface-based approaches}\label{secChgtAbr}
+In this section we discuss about the damage assessment techniques
+which can be applied when only two images (before/after) are available.\\
+
+As it has been shown in recent review works
+\cite{Coppin03,Lu04,Radke05,Richards05}, a relatively high number of
+methods exist, but most of them have been developed for optical and
+infrared sensors. Only a few recent works on change detection with
+radar images exist
+\cite{Stabel02,Bruzzone02b,Onana_2003,Inglada03,Derrode03,Bazi05}.
+However, the intrinsic limits of passive sensors, mainly related to
+their dependence on meteorological and illumination conditions, impose
+severe constraints for operational applications. The principal
+difficulties related to change detection are of four types:
+
+\begin{enumerate}
+\item In the case of radar images, the speckle noise makes the image
+ exploitation difficult.
+\item The geometric configuration of the image acquisition can produce
+ images which are difficult to compare.
+\item Also, the temporal gap between the two acquisitions an thus the
+ sensor aging and the inter-calibration are sources of variability
+ which are difficult to deal with.
+\item Finally, the normal evolution of the observed scenes must not be
+ confused with the changes of interest.
+\end{enumerate}
+
+The problem of detecting abrupt changes between a pair of images is
+the following: Let $I_{1},I_{2}$ be two images acquired at different
+dates $t_{1},t_{2}$; we aim at producing a thematic map which shows
+the areas where changes have taken place.
+
+Three main categories of methods exist:
+
+\begin{itemize}
+\item{Strategy $1$: Post Classification Comparison}
+
+The principle of this approach \cite{Deer_1998} is two obtain two
+land-use maps independently for each date and comparing them.
+
+
+
+\item{Strategy $2$: Joint classification}
+
+This method consists in producing the change map directly from a joint
+classification of both images.
+
+\item{Strategy $3$: Simple detectors}
+
+The last approach consists in producing an image of change likelihood
+(by differences, ratios or any other approach) and thresholding it in
+order to produce the change map.
+
+\end{itemize}
+
+
+Because of its simplicity and its low computation overhead, the third
+strategy is the one which has been chosen for the CNES processing
+chain presented in this document.
+
+
+
\section{Change Detection Framework}
\label{sec:ChangeDetectionFramework}
\input{ChangeDetectionFrameworkExample.tex}
@@ -8,10 +153,191 @@ Similarity, windows, 2 dates, etc.
\label{sec:SimpleDetectors}
\subsection{Mean Difference}
\label{sec:MeanDifference}
+
+The simplest change detector is based on the pixel-wise differencing
+of image values:
+\begin{equation}
+I_{D}(i,j)=I_{2}(i,j)-I_{1}(i,j).
+\end{equation}
+
+In order to make the algorithm robust to noise, one actually uses
+local means instead of pixel values.
+
\input{DiffChDet}
+
\subsection{Ratio Of Means}
\label{sec:RatioOfMeans}
+
+This detector is similar to the previous one except that it uses a
+ratio instead of the difference:
+\begin{equation}
+\displaystyle I_{R}(i,j) = \frac{\displaystyle I_{2}(i,j)}{\displaystyle I_{1}(i,j)}.
+\end{equation}
+
+The use of the ratio makes this detector robust to multiplicative
+noise which is a good model for the speckle phenomenon which is
+present in radar images.
+
+In order to have a bounded and normalized detector the following
+expression is actually used:
+
+
+\begin{equation}
+\displaystyle I_{R}(i,j) = 1 - min \left(\frac{\displaystyle I_{2}(i,j)}{\displaystyle I_{1}(i,j)},\frac{\displaystyle I_{1}(i,j)}{\displaystyle I_{2}(i,j)}\right).
+\end{equation}
+
+
\input{RatioChDet}
+
+
\section{Statistical Detectors}
\label{sec:StatisticalDetectors}
+
+%% \subsection{Local Correlation}
+
+%% The correlation coefficient measures the likelihood of a linear
+%% relationship between two random variables:
+%% \begin{equation}
+%% \begin{split}
+%% I_\rho(i,j) &= \frac{1}{N}\frac{\sum_{i,j}(I_1(i,j)-m_{I_1})(I_2(i,j)-m_{I_2})}{\sigma_{I_1}
+%% \sigma_{I_2}}\\
+%% & = \sum_{(I_1(i,j),I_2(i,j))}\frac{(I_1(i,j)-m_{I_1})(I_2(i,j)-m_{I_2})}{\sigma_{I_1}
+%% \sigma_{I_2}}p_{ij}
+%% \end{split}
+%% \end{equation}
+
+%% where $I_1(i,j)$ and $I_2(i,j)$ are the pixel values of the 2 images and
+%% $p_{ij}$ is the joint probability density. This is like using a linear model:
+%% \begin{equation}
+%% I_2(i,j) = (I_1(i,j)-m_{I_1})\frac{\sigma_{I_2}}{\sigma_{I_1}}+m_{I_2}
+%% \end{equation}
+%% for which we evaluate the likelihood with $p_{ij}$.
+
+%% With respect to the difference detector, this one will be robust to
+%% illumination changes.
+
+%% \subsection{Mutual Information}
+
+%% Other sophisticated change detectors can be used by applying some
+%% concepts of information theory. We have chosen to implement several
+%% detectors based on the mutual information measure
+%% \cite{Thevenaz2000,Inglada_2002}. This kind of measure needs for the
+%% estimation of the joint density probabilities for the pair of images
+%% to be compared. Depending on how this estimation is made, one can
+%% choose between robust but slow detectors or quick but less robust ones.\\
+
+%% The mutual information is a divergence (some kind of distance) between
+%% the joint probability $p_{1,2}$ and the product of marginal ones
+%% $p_1\cdot p_2$. Therefore, it is a measure of statistical dependence
+%% between the two images and can thus be understood as a generalization
+%% of the correlation coefficient. This means that it can be applied to
+%% the multi-sensor case.\\
+
+%% The divergence used is written as:
+%% \begin{equation}
+%% K(P,Q) = \int p \log\frac{p}{q},
+%% \end{equation}
+
+%% so the mutual information detector is written as:
+
+
+%% \begin{equation}
+%% I_{MI}(i,j) = \int p_{1,2} \log\frac{p_{1,2}}{p_1\cdot p_2}.
+%% \end{equation}
+
+
+%% \subsubsection{Joint histogram}
+%% In this version of the detector, a joint probability density $p_{ij}$ is
+%% estimated only once for the pair of images. This makes it a rather
+%% quick detector.
+%% \subsubsection{Local histogram}
+%% This version uses a local estimation of the probabilities in the
+%% neighborhood of each pixel. It is the slowest detector, but the most
+%% robust one.
+%% \subsubsection{Cumulant-based}
+
+%% This version is the quickest one, but it is only an approximation of the
+%% mutual information. Indeed, a probability density can be
+%% reconstructed from a series expansion of its cumulants. The
+%% cumulants are defined as follows:
+%% \begin{subequations}
+%% \begin{equation}
+%% E\left[\prod_{k \in N} X_k\right]=\sum_{N_1\cup\cdot\cdot\cdot \cup
+%% N_n=N}cum(X_k, k \in N_1)\cdot\cdot\cdot cum(X_k,k\in N_n)=\kappa_k,
+%% \end{equation}
+%% \begin{equation}
+%% cum(X_k, k\in N)=\sum_{N_1\cup\cdot\cdot\cdot \cup N_n=N}
+%% (-1)^{n-1}(n-1)!E\left[\prod_{k\in N_1} X_k \right]\cdot\cdot\cdot
+%% E\left[\prod_{k\in N_n} X_k \right],
+%% \end{equation}
+%% \end{subequations}
+
+%% For instance, one has
+
+%% \begin{equation}
+%% cum(X_1,X_2)=E(X_1,X_2)-(EX_1)(EX_2)=cov(X_1,X_2).
+%% \end{equation}
+
+%% \begin{equation}
+%% \begin{split}
+%% cum(X_1,X_2,X_3)=&E(X_1,X_2,X_3)-E(X_1,X_2)(EX_3)-E(X_1,X_3)(EX_2)\\
+%% & -E(X_2,X_3)(EX_1)+2(EX_1)(EX_2)(EX_3)
+%% \end{split}
+%% \end{equation}
+
+%% Using these cumulants, the series expansion of the probability density
+%% function $f(x)$ can be written as a modulation of the normalized
+%% Gaussian function $\Phi(x)$:
+
+
+%% \begin{equation}
+%% f(x) \approx \Phi(x)\left[ P_0(x) +
+%% P_1(x)\frac{1}{\sqrt{n}}+ P_2(x)\frac{1}{n} + ...+ P_r(x)\frac{1}{n^{r/2}}\right],
+%% \end{equation}
+%% with
+%% \begin{subequations}
+%% \begin{equation}
+%% P_0(x) = 1,
+%% \end{equation}
+%% \begin{equation}
+%% P_1(x) = \frac{\kappa_3}{3!}H_3(x),
+%% \end{equation}
+%% \begin{equation}
+%% P_2(x) = \frac{\kappa_4}{4!}H_4(x) + \frac{10\kappa_3^2}{6!}H_6(x),
+%% \end{equation}
+%% \end{subequations}
+%% and the Hermite polynomials
+%% \begin{subequations}
+%% \begin{equation}
+%% H_0(x) = 1,
+%% \end{equation}
+%% \begin{equation}
+%% H_1(x) = x,
+%% \end{equation}
+%% \begin{equation}
+%% H_2(x) = x^2 -1,
+%% \end{equation}
+%% \begin{equation}
+%% H_3(x) = x^3-3x.
+%% \end{equation}
+%% \end{subequations}
+
+%% When thiese approximations are used in the expression of the mutual
+%% information, one has the following result:
+
+%% \begin{equation}
+%% I_{IM}(i,j)({\underline Y})\approx \frac{1}{4}\sum_{kl\neq
+%% kk}\left(cum_2(Y_k,Y_l)\right)^2+\frac{1}{48}\sum_{klmn\neq
+%% kkkk}\left(cum_4(Y_k,Y_l,Y_m,Y_n)\right)^2,
+%% \label{kim}
+%% \end{equation}
+%% where $\{k,l,m,n\}$ can take the values 1 and 2 (the image index) and
+%% the cumulants are computed in the neighborhood if the pixel of
+%% coordinates $(i,j)$.
+
+
+
+
+
+
diff --git a/SoftwareGuide/Latex/HybridSegmentationMethods.tex b/SoftwareGuide/Latex/HybridSegmentationMethods.tex
index 22db963e471b81f4aa760b26df23955a5f7a19de..69cdcf60a083b6455a832633714ca19407751de7 100755
--- a/SoftwareGuide/Latex/HybridSegmentationMethods.tex
+++ b/SoftwareGuide/Latex/HybridSegmentationMethods.tex
@@ -196,9 +196,10 @@ deformable model segmentation methods.}
\subsubsection{Example of a Hybrid Segmentation Method}
\label{sec:HybridMethod1:Example}
-\ifitkFullVersion
-\input{HybridSegmentationFuzzyVoronoi.tex}
-\fi
+%\ifitkFullVersion
+%\input{HybridSegmentationFuzzyVoronoi.tex}
+%\fi
+