Day 4

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Paper title Dealing with Training Data Paucity: One-class versus Binary Classifiers for SAR-based Flood Detection
Authors
  1. Clara Lößl Universität Osnabrück - Institut für Informatik Speaker
  2. Tim Landwehr Universität Osnabrück - Institut für Informatik
  3. Antara Dasgupta Universität Osnabrück - Institut für Informatik
  4. Björn Waske Universität Osnabrück
Form of presentation Poster
Topics
  • A7. Hydrology and Water Cycle
    • A7.01 Inland Water Storage and Runoff: Modeling, In Situ Data and Remote Sensing
Abstract text Climate change increases the likelihood of catastrophic flood events, resulting in destruction of cropland and infrastructure, thereby threatening food security and exacerbating epidemics. These dangerous impacts highlight the need for rapid monitoring of inundation, which is necessary to estimate the dimensions of the disaster. An accurate satellite-based flood mapping can support the risk management cycle, from near-real-time rescue and response until post-event analysis. Current remote sensing techniques allow cheap, quick, and accurate flood classifications, using freely accessible satellite-data, for instance, from the Copernicus Sentinel satellites. Indeed, the Synthetic Aperture Radar (SAR) sensor on-board Sentinel-1 (S1), is uniquely suited to flood mapping due to its 24-hour weather independent imaging technology, and is widely used globally due to the open data availability. Binary classifications are widely used to extract flood inundation from SAR data, but due to the large discrepancy in prevalence of flood/non-flood classes in an S1 tile, finding adequate appropriate labelled samples to train classifiers is extremely challenging in addition to being time consuming. Furthermore, the process of training data collection is non-trivial due to a variety of uncertainties in SAR data originating from the underlying land-use and incorrect labeling could lead to gross misclassifications. For example, if the training data does not sufficiently represent the flood surface roughness diversity, large inundated tracts could be missed by the classifier. Consequently, training a binary can be expensive, slow, and compromise on accuracy, since precise labels for both classes are required despite only one class of interest.

One-class classifiers address this issue, by using only samples of the class of interest, i.e. the true positives, making them the perfect choice for flood classification. Even though one-class classifiers have outperformed classical binary classifiers for a variety of use-cases, surprisingly they have not been widely used so far in flood mapping literature. Accordingly, this study provides the first assessment of one-class classifiers for flood extent delineation from SAR data.

The study area is the coastal part of Beira, Mozambique, where the Cyclone Idai made landfall on 15th March 2019. Idai was the deadliest cyclone in the Southern hemisphere, affecting over 850.000 people and leading to a Cholera outbreak. S1 SAR data was used to classify the inundated area using Support Vector Machine (SVM) and Random Forest (RF) for the binary classification and one-class SVM (OCSVM) for the one-class classification. The data inputs and training data for both flood classifications were the same. For validation concurrent cloud-free Sentinel-2 (S2) optical-data were used.

Preliminary results suggest that one-class classifiers can perform equivalently or better than standard classifiers for flood detection from SAR images given similar volume of training data. Moreover, one-class classifiers offer the advantage of using limited training data and thus result in lower classifier training as well as processing time, without compromising on detection accuracy. Based on the results obtained in this first benchmarking study, the use of one-class classifiers for flood mapping should be further explored, for a robust performance assessment given different underlying land-uses and geographical regions.