Difference between revisions of "ALGORITHM2"

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(Created page with "caption [Home] - [Algorithms] ---- '''HASARD''' is a probabilistic flood mapping algorithm developed by [https:/...")
 
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'''HASARD''' is a probabilistic flood mapping algorithm developed by [https://www.list.lu LIST] based on [https://en.wikipedia.org/wiki/Bayesian_inference Bayesian inference]. <br>
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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.
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Following the procedure introduced in Giustarini et al. (2016) <sup>[1]</sup> the probability of each pixel in any newly acquired SAR image being flooded given its measured backscatter value is estimated in near-real time.<br>
 
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The approach is based on the two distribution functions of backscatter values attributed to flooded and non-flooded terrain in the area covered by the image. <br>
 
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The procedure to calibrate the four parameters of the [https://en.wikipedia.org/wiki/Gaussian_function#:~:text=The%20graph%20of%20a%20Gaussian,symmetric%20%22bell%20curve%22%20shape.&text=Gaussian%20functions%20are%20often%20used,variance%20%CF%83%202%20%3D%20c%202. Gaussian probability distribution functions (PDFs)] based on the histograms of every newly acquired SAR image is explained in detail in Chini et al. (2017)<sup>[2]</sup>. <br>
 
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When the parameters of the PDFs are known, the conditional probability of a pixel being flooded given its backscatter value can be derived using [https://en.wikipedia.org/wiki/Bayes%27_theorem Bayes’ theorem].<br>
 
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Its computation, thus, requires both the probability distributions of backscatter values of flooded and non-flooded pixels and the prior probabilities of a pixel being flooded and non-flooded. <br>
 
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In the Bayesian framework, the prior probability ''p(F)'' of a pixel being flooded corresponds to the probability of it being flooded prior to the backscatter value being measured. <br>
 
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In principle, for a given pixel, any information other than the backscatter value of the pixel itself, ''σ0'', can be used to estimate these prior probabilities. <br>
 
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However, when no such ancillary data is available, the simplest choice is to use a non-informative prior, where ''p(F) = 0.5'', and where flooded and non-flooded pixels are equally likely a priori. <br>
 
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Findings in Giustarini et al. (2016)<sup>[1]</sup> showed that the non-informative prior leads to acceptable performance in terms of reliability and little is to be gained by attempting to estimate prior probabilities from other sources of data.<br>
 
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The non-informative priori is, thus, considered an acceptable choice, especially in the absence of any other exploitable data. It also worth mentioning that [https://en.wikipedia.org/wiki/Bayesian_inference Bayesian inference] is applied independently to each pixel, regardless of its spatial location.<br>
 
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Preliminary masking with the exclusion layer and the reference water extent layer is undertaken so that no probabilities are computed for those areas. <br>
 
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Probability map values range from 0 and 1.
 
  
  

Revision as of 14:52, 9 March 2021

caption

[Home] - [Algorithms]

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.


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


References

[1] Giustarini, L., Hostache, R., Kavetski, D., Chini, M., Corato, G., Schlaffer, S., and Matgen P. (2016). Probabilistic flood mapping using Synthetic Aperture Radar data, IEEE Transactions on Geoscience and Remote Sensing, 54 (12), 6958-6969
download Giustarini et al. (2016)


[2] Chini M., Hostache R., Giustarini L., Matgen P., A Hierarchical Split-Based Approach for parametric thresholding of SAR images: flood inundation as a test case, IEEE Transactions on Geoscience and Remote Sensing, 55 (12), 6975-6988, 2017.
download Chini et al. (2017)


[Home] - [Algorithms]

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