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Remote Sens. 2017, 9(6), 617; doi:10.3390/rs9060617

Flood Inundation Mapping from Optical Satellite Images Using Spatiotemporal Context Learning and Modest AdaBoost

1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium
4
Interuniversity Microelectronics Centre (IMEC), 3001 Heverlee, Belgium
*
Author to whom correspondence should be addressed.
Academic Editors: Qi Wang, Nicolas H. Younan, Carlos López-Martínez and Prasad S. Thenkabail
Received: 20 March 2017 / Revised: 8 June 2017 / Accepted: 10 June 2017 / Published: 16 June 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
View Full-Text   |   Download PDF [22900 KB, uploaded 16 June 2017]   |  

Abstract

Due to its capacity for temporal and spatial coverage, remote sensing has emerged as a powerful tool for mapping inundation. Many methods have been applied effectively in remote sensing flood analysis. Generally, supervised methods can achieve better precision than unsupervised. However, human intervention makes its results subjective and difficult to obtain automatically, which is important for disaster response. In this work, we propose a novel procedure combining spatiotemporal context learning method and Modest AdaBoost classifier, which aims to extract inundation in an automatic and accurate way. First, the context model was built with images to calculate the confidence value of each pixel, which represents the probability of the pixel remaining unchanged. Then, the pixels with the highest probabilities, which we define as ‘permanent pixels’, were used as samples to train the Modest AdaBoost classifier. By applying the strong classifier to the target scene, an inundation map can be obtained. The proposed procedure is validated using two flood cases with different sensors, HJ-1A CCD and GF-4 PMS. Qualitative and quantitative evaluation results showed that the proposed procedure can achieve accurate and robust mapping results. View Full-Text
Keywords: inundation mapping; flood; optical sensors; spatiotemporal context learning; Modest AdaBoost; HJ-1A/B CCD; GF-4 PMS inundation mapping; flood; optical sensors; spatiotemporal context learning; Modest AdaBoost; HJ-1A/B CCD; GF-4 PMS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Liu, X.; Sahli, H.; Meng, Y.; Huang, Q.; Lin, L. Flood Inundation Mapping from Optical Satellite Images Using Spatiotemporal Context Learning and Modest AdaBoost. Remote Sens. 2017, 9, 617.

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