Next Article in Journal
Submarine and Subaerial Morphological Changes Associated with the 2014 Eruption at Stromboli Island
Previous Article in Journal
Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations
Article

Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston

1
Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
2
Institute for Environmental Science and Geography, University of Potsdam, 14476 Potsdam-Golm, Germany
3
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstr. 8-10, A-1040 Vienna, Austria
4
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), D-82234 Wessling, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Angelica Tarpanelli
Remote Sens. 2021, 13(11), 2042; https://doi.org/10.3390/rs13112042
Received: 27 April 2021 / Revised: 14 May 2021 / Accepted: 17 May 2021 / Published: 22 May 2021
Flood masks are among the most common remote sensing products, used for rapid crisis information and as input for hydraulic and impact models. Despite the high relevance of such products, vegetated and urban areas are still unreliably mapped and are sometimes even excluded from analysis. The information content of synthetic aperture radar (SAR) images is limited in these areas due to the side-looking imaging geometry of radar sensors and complex interactions of the microwave signal with trees and urban structures. Classification from SAR data can only be optimized to reduce false positives, but cannot avoid false negatives in areas that are essentially unobservable to the sensor, for example, due to radar shadows, layover, speckle and other effects. We therefore propose to treat satellite-based flood masks as intermediate products with true positives, and unlabeled cells instead of negatives. This corresponds to the input of a positive-unlabeled (PU) learning one-class classifier (OCC). Assuming that flood extent is at least partially explainable by topography, we present a novel procedure to estimate the true extent of the flood, given the initial mask, by using the satellite-based products as input to a PU OCC algorithm learned on topographic features. Additional rainfall data and distance to buildings had only minor effect on the models in our experiments. All three of the tested initial flood masks were considerably improved by the presented procedure, with obtainable increases in the overall κ score ranging from 0.2 for a high quality initial mask to 0.7 in the best case for a standard emergency response product. An assessment of κ for vegetated and urban areas separately shows that the performance in urban areas is still better when learning from a high quality initial mask. View Full-Text
Keywords: urban flood mapping; flood mask; one-class classification; pu learning; extrapolation; topographic features urban flood mapping; flood mask; one-class classification; pu learning; extrapolation; topographic features
Show Figures

Graphical abstract

MDPI and ACS Style

Brill, F.; Schlaffer, S.; Martinis, S.; Schröter, K.; Kreibich, H. Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston. Remote Sens. 2021, 13, 2042. https://doi.org/10.3390/rs13112042

AMA Style

Brill F, Schlaffer S, Martinis S, Schröter K, Kreibich H. Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston. Remote Sensing. 2021; 13(11):2042. https://doi.org/10.3390/rs13112042

Chicago/Turabian Style

Brill, Fabio; Schlaffer, Stefan; Martinis, Sandro; Schröter, Kai; Kreibich, Heidi. 2021. "Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston" Remote Sens. 13, no. 11: 2042. https://doi.org/10.3390/rs13112042

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop