Difference between revisions of "ALGORITHM2"
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| − | [https://www.tuwien.at/ TUW]'s flood detection algorithm (provisionally, '''Algorithm2''') is a fuzzy-logic based water class membership assignment, is a part of a flood classification refinement step described in Martinis et al. (2015) and Twele et al. (2016). It aims to exclude water-lookalikes and to reduce underestimations from initial classification by constructing a fuzzy set that consists of (a) the backscatter level, (b) the elevation of an image element in comparison to the mean elevation of the initially derived water areas, (c) topographic slope information, and (d) the size of an individual flood object; degree of an element’s membership to the class water is determined by standard S and Z membership functions. The average of the individual membership degrees is computed for each pixel in order to combine all fuzzy elements into a single composite fuzzy set Figure 20. | + | [https://www.tuwien.at/ TUW]'s flood detection algorithm (provisionally, '''Algorithm2''') is a fuzzy-logic based water class membership assignment, is a part of a flood classification refinement step described in Martinis et al. (2015)<sup>[1]</sup> and Twele et al. (2016)<sup>[2]</sup>. It aims to exclude water-lookalikes and to reduce underestimations from initial classification by constructing a fuzzy set that consists of (a) the backscatter level, (b) the elevation of an image element in comparison to the mean elevation of the initially derived water areas, (c) topographic slope information, and (d) the size of an individual flood object; degree of an element’s membership to the class water is determined by standard S and Z membership functions. The average of the individual membership degrees is computed for each pixel in order to combine all fuzzy elements into a single composite fuzzy set Figure 20. |
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====<u>''References''</u>==== | ====<u>''References''</u>==== | ||
| − | <sup>[1] | + | <sup>[1]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. <br> |
| − | [https:// | + | [https://www.sciencedirect.com/science/article/pii/S0924271614001981 <u>download Martinis et al. (2015)</u>] |
| − | <sup>[2]</sup> | + | <sup>[2]</sup> 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.<br> |
| − | [https:// | + | [https://www.tandfonline.com/doi/abs/10.1080/01431161.2016.1192304 <u>download Twele et al. (2016)</u>] |
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[[GFMS | [Home]]] - [[ALGORITHMS | [Algorithms]]] | [[GFMS | [Home]]] - [[ALGORITHMS | [Algorithms]]] | ||
Revision as of 14:58, 9 March 2021
TUW's flood detection algorithm (provisionally, Algorithm2) is a fuzzy-logic based water class membership assignment, is a part of a flood classification refinement step described in Martinis et al. (2015)[1] and Twele et al. (2016)[2]. It aims to exclude water-lookalikes and to reduce underestimations from initial classification by constructing a fuzzy set that consists of (a) the backscatter level, (b) the elevation of an image element in comparison to the mean elevation of the initially derived water areas, (c) topographic slope information, and (d) the size of an individual flood object; degree of an element’s membership to the class water is determined by standard S and Z membership functions. The average of the individual membership degrees is computed for each pixel in order to combine all fuzzy elements into a single composite fuzzy set Figure 20.
For further details the Reader is referred to the dedicated section of the Product Description Document: https://www.gfm_pdd.org/Algorithm2
References
[1]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.
download Martinis et al. (2015)
[2] 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.
download Twele et al. (2016)
