TY - JOUR
T1 - Sub-pixel flood inundation mapping from multispectral remotely sensed images based on discrete particle swarm optimization
AU - Li, Linyi
AU - Chen, Yun
AU - Yu, Xin
AU - Liu, Rui
AU - Huang, Chang
N1 - Funding Information:
This paper was supported by the National Natural Science Foundation of China (Grant No. 41371343 and Grant No. 41001255 ) and the scholarship provided by the China Scholarship Council . The authors would like to thank the Editor-in-Chief, the Associate Editor, and anonymous reviewers for the helpful comments and suggestions that improved this paper. The authors also wish to thank their colleagues Susan Cuddy and Catherine Ticehurst for their helpful discussions and constructive suggestions.
Publisher Copyright:
© 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
PY - 2015/3/1
Y1 - 2015/3/1
N2 - The study of flood inundation is significant to human life and social economy. Remote sensing technology has provided an effective way to study the spatial and temporal characteristics of inundation. Remotely sensed images with high temporal resolutions are widely used in mapping inundation. However, mixed pixels do exist due to their relatively low spatial resolutions. One of the most popular approaches to resolve this issue is sub-pixel mapping. In this paper, a novel discrete particle swarm optimization (DPSO) based sub-pixel flood inundation mapping (DPSO-SFIM) method is proposed to achieve an improved accuracy in mapping inundation at a sub-pixel scale. The evaluation criterion for sub-pixel inundation mapping is formulated. The DPSO-SFIM algorithm is developed, including particle discrete encoding, fitness function designing and swarm search strategy. The accuracy of DPSO-SFIM in mapping inundation at a sub-pixel scale was evaluated using Landsat ETM. +. images from study areas in Australia and China. The results show that DPSO-SFIM consistently outperformed the four traditional SFIM methods in these study areas. A sensitivity analysis of DPSO-SFIM was also carried out to evaluate its performances. It is hoped that the results of this study will enhance the application of medium-low spatial resolution images in inundation detection and mapping, and thereby support the ecological and environmental studies of river basins.
AB - The study of flood inundation is significant to human life and social economy. Remote sensing technology has provided an effective way to study the spatial and temporal characteristics of inundation. Remotely sensed images with high temporal resolutions are widely used in mapping inundation. However, mixed pixels do exist due to their relatively low spatial resolutions. One of the most popular approaches to resolve this issue is sub-pixel mapping. In this paper, a novel discrete particle swarm optimization (DPSO) based sub-pixel flood inundation mapping (DPSO-SFIM) method is proposed to achieve an improved accuracy in mapping inundation at a sub-pixel scale. The evaluation criterion for sub-pixel inundation mapping is formulated. The DPSO-SFIM algorithm is developed, including particle discrete encoding, fitness function designing and swarm search strategy. The accuracy of DPSO-SFIM in mapping inundation at a sub-pixel scale was evaluated using Landsat ETM. +. images from study areas in Australia and China. The results show that DPSO-SFIM consistently outperformed the four traditional SFIM methods in these study areas. A sensitivity analysis of DPSO-SFIM was also carried out to evaluate its performances. It is hoped that the results of this study will enhance the application of medium-low spatial resolution images in inundation detection and mapping, and thereby support the ecological and environmental studies of river basins.
KW - Discrete particle swarm optimization
KW - Flood inundation
KW - Multispectral remotely sensed images
KW - Sub-pixel mapping
UR - http://www.scopus.com/inward/record.url?scp=84919786757&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2014.11.006
DO - 10.1016/j.isprsjprs.2014.11.006
M3 - Article
AN - SCOPUS:84919786757
SN - 0924-2716
VL - 101
SP - 10
EP - 21
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
ER -