Document Type

Article

Publication Date

2020

DOI

10.3390/rs12152455

Publication Title

Remote Sensing

Volume

12

Issue

15

Pages

17 pp.

Abstract

Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and multi-temporal image dataset. Specifically, we used SPOT-5 and RADAR images from the flood event that occurred in November 2000 in Gloucester, UK. Experimental results show that the domain adaptation-based approach, semi-supervised domain adaptation (SSDA) with 20 labeled data samples, achieved slightly better values of the area under the precision-recall (PR) curve (AUC) of 0.9173 and F1 score of 0.8846 than those by traditional machine approaches. However, SSDA required much less labor for ground-truth labeling and should be recommended in practice.

Comments

© 2020 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

Original Publication Citation

Islam, K. A., Uddin, M. S., Kwan, C., & Li, J. (2020). Flood detection using multi-modal and multi-temporal images: A comparative study. Remote Sensing, 12(15), 17 pp., Article 2455. https://doi.org/10.3390/rs12152455

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