ALGORITHM3 - Revision history

Admin: /* References */

2021-03-23T11:34:59Z

‎References

Admin at 11:14, 10 March 2021

2021-03-10T11:14:23Z

Admin at 11:05, 10 March 2021

2021-03-10T11:05:11Z

Admin: /* References */

2021-03-10T10:53:00Z

‎References

Admin at 10:52, 10 March 2021

2021-03-10T10:52:08Z

Admin at 10:46, 10 March 2021

2021-03-10T10:46:01Z

Admin at 10:45, 10 March 2021

2021-03-10T10:45:43Z

Admin: Created page with "caption [Home] - [Algorithms] ---- [https://www.tuwien.at/ TU Wien]’s S-1 flood mapping algorithm exploits the..."

2021-03-10T10:40:30Z

Created page with "caption [Home] - [Algorithms] ---- [https://www.tuwien.at/ TU Wien]’s S-1 flood mapping algorithm exploits the..."

New page

[[File:example7.jpg|right|100px|caption]]
[[GFMS | [Home]]] - [[ALGORITHMS | [Algorithms]]]
----

[https://www.tuwien.at/ TU Wien]’s S-1 flood mapping algorithm exploits the time series of historical backscatter measurements to generate model parameters for permanent water bodies and for each individual location on land. 
More specifically, the model parameter database comprises of the backscatter distribution of each land pixel under unflooded conditions, as well as the backscatter distribution of permanent water bodies at various incidence angles. In order to classify a pixel of incoming pre-processed Sentinel-1 scene as flooded or non-flooded, the posterior probability of the actual backscatter value belonging to each class is computed. 

The maximum posterior probability is used to classify the pixel based on [https://en.wikipedia.org/wiki/Decision_theory Bayes Decision Rule], where the uncertainty of the class assignment is given by the minimum posterior probability of the other class. With this approach, each pixel is assigned membership in a certain class, along with an uncertainty value ranging between ''0'' and ''1''. 

The uncertainty is minimized as with improved separability between the two backscatter signature distributions and is higher where the distributions overlap (Figure 21).
[[File:alg3.jpg|centre|800px|border]]

Consequently, pixels located along flood boundaries, where class assignment is generally more challenging, are associated with higher degrees of uncertainty. Finally, to convert the uncertainty values to percentages, the uncertainty values are linearly scaled to fall within the range of [0, 100].

====''Workflow of the algorithm''====
[[File:alg3_wf.jpg|centre|800px|border]]

'''For further details the Reader is referred to the dedicated section of the Product Description Document: https://www.gfm_pdd.org/Algorithm2'''

====''References''====
[1] [https://www.sciencedirect.com/science/article/pii/S0924271614001981 Martinis, S., Twele, A., Kersten, J., (2015). A fully automated TerraSAR-X based flood service. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 203-212.] 

[2] [https://www.tandfonline.com/doi/abs/10.1080/01431161.2016.1192304 Twele, A., Cao, W., Plank, S., Martinis, S., 2016. Sentinel-1 based flood mapping: a fully automated processing chain. International Journal of Remote Sensing, 37 (13), 2990-3004.]

----
[[GFMS | [Home]]] - [[ALGORITHMS | [Algorithms]]]

← Older revision		Revision as of 11:34, 23 March 2021
Line 28:		Line 28:

	====<u>''References''</u>====		====<u>''References''</u>====
−	<sup>[1]</sup> ~~<span style=background:yellow>MISSING</span>~~	+	<sup>[1]</sup> Bauer-Marschallinger, B., Cao, S., Wagner, W., Navacchi, C., Tupas, M.E., Roth, F., Melzer, T., Freeman, V. (in preparation). Satellite-based Flood Mapping through Bayesian Inference from Sentinel-1 SAR Datacube. Remote Sensing.

← Older revision		Revision as of 11:14, 10 March 2021
Line 18:		Line 18:
	[[File:alg3_wf.jpg\|centre\|800px\|border]]		[[File:alg3_wf.jpg\|centre\|800px\|border]]

		+
		+	====<u>''In a nutshell''</u>====
		+	'''Algorithm''' 3 fully exploits the '''entire Sentinel-1 signal history''' within the data cube, realized by a set of '''a-priori computed statistical parameters layers''' that provide a highly accurate '''characterization of Earth’s land surface at pixel level''', as well as of the '''incidence angle dependency''' in respect to the Sentinel-1 mission. With those parameters as input, and with the '''mathematical legacy of Bayes''', the water delineation procedure can be designed '''computationally relatively slim''' and is hence '''most suitable for global operations in near-real time'''.

@@ Line 8: / Line 8: @@
 The maximum posterior probability is used to classify the pixel based on [https://en.wikipedia.org/wiki/Decision_theory Bayes Decision Rule], where the uncertainty of the class assignment is given by the minimum posterior probability of the other class. With this approach, each pixel is assigned membership in a certain class, along with an uncertainty value ranging between ''0'' and ''1''.<br>
-The uncertainty is minimized as with improved separability between the two backscatter signature distributions and is higher where the distributions overlap (Figure 21).
+The uncertainty is minimized as with improved separability between the two backscatter signature distributions and is higher where the distributions overlap (see figure below).
 [[File:alg3.jpg|centre|800px|border]]

@@ Line 25: / Line 25: @@
 ====<u>''References''</u>====
-<sup>[1]</sup> <span style=background:red>MISSING</span>
+<sup>[1]</sup> <span style=background:yellow>MISSING</span>
-<sup>[2]</sup> <span style=background:red>MISSING</span>
+<sup>[2]</sup> <span style=background:yellow>MISSING</span>
 ----
 [[GFMS | [Home]]] - [[ALGORITHMS | [Algorithms]]]

@@ Line 20: / Line 20: @@
-'''For further details the Reader is referred to the dedicated section of the Product Description Document: https://www.gfm_pdd.org/Algorithm2'''
+'''For further details the Reader is referred to the dedicated section of the Product Description Document: https://www.gfm_pdd.org/Algorithm3'''
 ====<u>''References''</u>====
-<sup>[1]</sup> [https://www.sciencedirect.com/science/article/pii/S0924271614001981 <u>Martinis, S., Twele, A., Kersten, J., (2015). A fully automated TerraSAR-X based flood service. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 203-212.</u>] <br>
+<sup>[1]</sup> <span style=background:red>MISSING</span>
-<sup>[2]</sup> [https://www.tandfonline.com/doi/abs/10.1080/01431161.2016.1192304 <u>Twele, A., Cao, W., Plank, S., Martinis, S., 2016. Sentinel-1 based flood mapping: a fully automated processing chain. International Journal of Remote Sensing, 37 (13), 2990-3004.</u>]
+<sup>[2]</sup> <span style=background:red>MISSING</span>
 ----
 [[GFMS | [Home]]] - [[ALGORITHMS | [Algorithms]]]

@@ Line 13: / Line 13: @@
   ''Water PDF, non-water PDF, water posterior probability and non-water posterior probability with equal priors (0.5) for a flooded pixel. The backscatter measurement equals to -15dB. Thus, the pixel is classified as water, because the posterior probability of water is larger than non-water. The uncertainty is then the posterior probability of non-water, reflecting the overlap of both PDFs.''
-Consequently, pixels located along flood boundaries, where class assignment is generally more challenging, are associated with higher degrees of uncertainty. Finally, to convert the uncertainty values to percentages, the uncertainty values are linearly scaled to fall within the range of [0, 100].
+Consequently, pixels located along flood boundaries, where class assignment is generally more challenging, are associated with higher degrees of uncertainty. Finally, to convert the uncertainty values to percentages, the uncertainty values are linearly scaled to fall within the range of ''[0, 100]''.
 ====<u>''Workflow of the algorithm''</u>====