ALGORITHMS - Revision history

Admin: /* Algorithms description */

2021-03-25T10:45:49Z

‎Algorithms description

2021-03-10T12:20:09Z

2021-03-10T10:52:32Z

2021-03-10T09:12:50Z

2021-03-10T09:11:10Z

2021-03-09T14:37:51Z

2021-03-04T14:11:25Z

2021-03-04T14:09:26Z

‎Algorithms description

@@ Line 57: / Line 57: @@
 |Yes
 |No, the integration of a low backscatter exclusion mask based on S-1 time-series data (produced offline) can be integrated optionally
-|Yes
+|Yes, parametrisation through multi-year time series
 |-
 |'''Exploitation of textual information through region growing'''

← Older revision		Revision as of 12:20, 10 March 2021
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−	'''GFM''''s flood products are based on an ''ensemble approach'' integrating three robust, cutting edge algorithms developed independently by three leading research teams. <br>	+	'''GFM''' 's flood products are based on an ''ensemble approach'' integrating three robust, cutting edge algorithms developed independently by three leading research teams. <br>
	The motivation for choosing such a methodology is to substantially improve accuracy of the derived Sentinel-1 flood and water extent maps and to build a high degree of redundancy into the production service.		The motivation for choosing such a methodology is to substantially improve accuracy of the derived Sentinel-1 flood and water extent maps and to build a high degree of redundancy into the production service.

@@ Line 18: / Line 18: @@
 For these very reasons, Users have access to [[S-1_Observed_Flood_Extent | '' '''consensus flood maps''']] '' where a pixel is marked as ''flooded'' when at least two algorithms classify it as water. <br>
-Accordingly, the implemented quality assurance procedures (see <span style=background:lightblue>'''INSERT REFERENCE'''</span>) allow for differentiating between classification errors that can be attributed to shortcomings of individual algorithms and errors that are inherent to the SAR sensing instruments and their difficulty to capture the appearance or disappearance of surface water in particular situations.
+Accordingly, the implemented quality assurance procedures (see <span style=background:yellow>'''INSERT REFERENCE'''</span>) allow for differentiating between classification errors that can be attributed to shortcomings of individual algorithms and errors that are inherent to the SAR sensing instruments and their difficulty to capture the appearance or disappearance of surface water in particular situations.
 ===Algorithms description===

@@ Line 31: / Line 31: @@
+!
 !Hasard <br> [[File:list.jpg|centre|50px]]
-!Algorithm2 <br> [[File:dlr.jpg|centre|20px]]
+!Algorithm2 <br> [[File:tuw.jpg|centre|20px]]
-!Algorithm3 <br> [[File:tuw.jpg|centre|30px]]
+!Algorithm3 <br> [[File:dlr.jpg|centre|30px]]
 |-
 |style="width: 20%" | '''Application domain'''

@@ Line 18: / Line 18: @@
 For these very reasons, Users have access to [[S-1_Observed_Flood_Extent | '' '''consensus flood maps''']] '' where a pixel is marked as ''flooded'' when at least two algorithms classify it as water. <br>
-Accordingly, the implemented quality assurance procedures (see '''INSERT REFERENCE''') allow for differentiating between classification errors that can be attributed to shortcomings of individual algorithms and errors that are inherent to the SAR sensing instruments and their difficulty to capture the appearance or disappearance of surface water in particular situations.
+Accordingly, the implemented quality assurance procedures (see <span style=background:lightblue>'''INSERT REFERENCE'''</span>) allow for differentiating between classification errors that can be attributed to shortcomings of individual algorithms and errors that are inherent to the SAR sensing instruments and their difficulty to capture the appearance or disappearance of surface water in particular situations.
 ===Algorithms description===

@@ Line 83: / Line 83: @@
 |Yes (based on fuzzy logic)
 |Yes (based on the Bayesian posterior probability)
 |-
 |'''Outstanding/differentiating features'''

@@ Line 1: / Line 1: @@
 [[GFMS | [Home]]]
 ----
-[[File:example7.jpg|right|100px|caption]]
 '''GFM''''s flood products are based on an ''ensemble approach'' integrating three robust, cutting edge algorithms developed independently by three leading research teams. <br>

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 |'''VALUTARE DA BIBLIOGRAFIA'''
 |-
-|12. [[Affected Land Cover]]
+|'''Outstanding/differentiating features'''
-|Land cover type flooded
+|Hierarchical split-based approach enabling re-calibration of parameters in NRT based on most recent pair of S-1 images
-|''raster (Geotiff)''
+|Fuzzy logic-based approach enabling a post classification and region growing taking advantage of topography-derived indices in addition to SAR backscatter
+|Exploiting per-pixel full Sentinel-1 signal history in data cube; enabling a very fast and scalable production of flood and water extent maps through pre-computed global parameters at high quality
 |-
-|13. [[Affected Land Cover]]
+|'''Additional information'''
-|Land cover type flooded
+|[[HASARD | further details on HASARD]]
-|''raster (Geotiff)''
+|[[ALGORITHM2 | further details on Algorithm2]]
+|[[ALGORITHM3 | further details on Algorithm3]]
 |-
 |}