Detecting tents to estimate the displaced populations for post-disaster relief using high resolution satellite imagery

Shifeng Wang*, Emily So, Pete Smith

*Corresponding author for this work

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Estimating the number of refugees and internally displaced persons is important for planning and managing an efficient relief operation following disasters and conflicts. Accurate estimates of refugee numbers can be inferred from the number of tents. Extracting tents from high-resolution satellite imagery has recently been suggested. However, it is still a significant challenge to extract tents automatically and reliably from remote sensing imagery. This paper describes a novel automated method, which is based on mathematical morphology, to generate a camp map to estimate the refugee numbers by counting tents on the camp map. The method is especially useful in detecting objects with a clear shape, size, and significant spectral contrast with their surroundings. Results for two study sites with different satellite sensors and different spatial resolutions demonstrate that the method achieves good performance in detecting tents. The overall accuracy can be up to 81% in this study. Further improvements should be possible if over-identified isolated single pixel objects can be filtered. The performance of the method is impacted by spectral characteristics of satellite sensors and image scenes, such as the extent of area of interest and the spatial arrangement of tents. It is expected that the image scene would have a much higher influence on the performance of the method than the sensor characteristics. (C) 2014 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)87-93
Number of pages7
JournalInternational journal of applied earth observation and geoinformation
Volume36
Early online date5 Dec 2014
DOIs
Publication statusPublished - Apr 2015

Keywords

  • remote sensing
  • disaster
  • refugee
  • automate
  • panchromatic
  • relief operation
  • remotely-sensed data
  • information
  • space

Cite this

@article{46fa5163d32849e29b8e0d23ebc76bb6,
title = "Detecting tents to estimate the displaced populations for post-disaster relief using high resolution satellite imagery",
abstract = "Estimating the number of refugees and internally displaced persons is important for planning and managing an efficient relief operation following disasters and conflicts. Accurate estimates of refugee numbers can be inferred from the number of tents. Extracting tents from high-resolution satellite imagery has recently been suggested. However, it is still a significant challenge to extract tents automatically and reliably from remote sensing imagery. This paper describes a novel automated method, which is based on mathematical morphology, to generate a camp map to estimate the refugee numbers by counting tents on the camp map. The method is especially useful in detecting objects with a clear shape, size, and significant spectral contrast with their surroundings. Results for two study sites with different satellite sensors and different spatial resolutions demonstrate that the method achieves good performance in detecting tents. The overall accuracy can be up to 81{\%} in this study. Further improvements should be possible if over-identified isolated single pixel objects can be filtered. The performance of the method is impacted by spectral characteristics of satellite sensors and image scenes, such as the extent of area of interest and the spatial arrangement of tents. It is expected that the image scene would have a much higher influence on the performance of the method than the sensor characteristics. (C) 2014 Elsevier B.V. All rights reserved.",
keywords = "remote sensing, disaster, refugee, automate, panchromatic, relief operation, remotely-sensed data, information, space",
author = "Shifeng Wang and Emily So and Pete Smith",
note = "Acknowledgements: This research was partly supported by the European Commission under FP7 (Seventh Framework Programme): “SENSUM: Framework to Intergrade Space-based and in-situ sENSing for dynamic vUlnerability and recovery Monitoring” (312972). We gratefully acknowledge the helpful comments from Michael Ramage, Dilkushi de Alwis Pitts, and the anonymous referees.",
year = "2015",
month = "4",
doi = "10.1016/j.jag.2014.11.013",
language = "English",
volume = "36",
pages = "87--93",
journal = "International journal of applied earth observation and geoinformation",
issn = "0303-2434",
publisher = "International Institute for Aerial Survey and Earth Sciences",

}

TY - JOUR

T1 - Detecting tents to estimate the displaced populations for post-disaster relief using high resolution satellite imagery

AU - Wang, Shifeng

AU - So, Emily

AU - Smith, Pete

N1 - Acknowledgements: This research was partly supported by the European Commission under FP7 (Seventh Framework Programme): “SENSUM: Framework to Intergrade Space-based and in-situ sENSing for dynamic vUlnerability and recovery Monitoring” (312972). We gratefully acknowledge the helpful comments from Michael Ramage, Dilkushi de Alwis Pitts, and the anonymous referees.

PY - 2015/4

Y1 - 2015/4

N2 - Estimating the number of refugees and internally displaced persons is important for planning and managing an efficient relief operation following disasters and conflicts. Accurate estimates of refugee numbers can be inferred from the number of tents. Extracting tents from high-resolution satellite imagery has recently been suggested. However, it is still a significant challenge to extract tents automatically and reliably from remote sensing imagery. This paper describes a novel automated method, which is based on mathematical morphology, to generate a camp map to estimate the refugee numbers by counting tents on the camp map. The method is especially useful in detecting objects with a clear shape, size, and significant spectral contrast with their surroundings. Results for two study sites with different satellite sensors and different spatial resolutions demonstrate that the method achieves good performance in detecting tents. The overall accuracy can be up to 81% in this study. Further improvements should be possible if over-identified isolated single pixel objects can be filtered. The performance of the method is impacted by spectral characteristics of satellite sensors and image scenes, such as the extent of area of interest and the spatial arrangement of tents. It is expected that the image scene would have a much higher influence on the performance of the method than the sensor characteristics. (C) 2014 Elsevier B.V. All rights reserved.

AB - Estimating the number of refugees and internally displaced persons is important for planning and managing an efficient relief operation following disasters and conflicts. Accurate estimates of refugee numbers can be inferred from the number of tents. Extracting tents from high-resolution satellite imagery has recently been suggested. However, it is still a significant challenge to extract tents automatically and reliably from remote sensing imagery. This paper describes a novel automated method, which is based on mathematical morphology, to generate a camp map to estimate the refugee numbers by counting tents on the camp map. The method is especially useful in detecting objects with a clear shape, size, and significant spectral contrast with their surroundings. Results for two study sites with different satellite sensors and different spatial resolutions demonstrate that the method achieves good performance in detecting tents. The overall accuracy can be up to 81% in this study. Further improvements should be possible if over-identified isolated single pixel objects can be filtered. The performance of the method is impacted by spectral characteristics of satellite sensors and image scenes, such as the extent of area of interest and the spatial arrangement of tents. It is expected that the image scene would have a much higher influence on the performance of the method than the sensor characteristics. (C) 2014 Elsevier B.V. All rights reserved.

KW - remote sensing

KW - disaster

KW - refugee

KW - automate

KW - panchromatic

KW - relief operation

KW - remotely-sensed data

KW - information

KW - space

U2 - 10.1016/j.jag.2014.11.013

DO - 10.1016/j.jag.2014.11.013

M3 - Article

VL - 36

SP - 87

EP - 93

JO - International journal of applied earth observation and geoinformation

JF - International journal of applied earth observation and geoinformation

SN - 0303-2434

ER -