Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013)

Water Depth Inversion in Haikou Bay Based on TM Image

Authors
Ruan Kun, Zhu Shouxian, Zhang Xiaodong, Zeng Wenhua
Corresponding Author
Ruan Kun
Available Online August 2013.
DOI
10.2991/rsete.2013.186How to use a DOI?
Keywords
Water depth inversion, Remote sensing, TM, Haikou Bay
Abstract

Remote sensing has been proved to be a useful technique in measuring water depth in coastal zone. This paper takes Haikou Bay as the study area. The spectral analysis of the correlation index between TM image and the observed water depth from chart is made. TM3 gives the largest absolute correlation index in the all single spectra, and TM1/TM3 gives the larger absolute correlation index than other band ratios. Then some models using linear, logarithmic, power exponential and exponential equations have been made for water depth inversion, in which TM3, TM1/TM3 have been used. The exponential model based on the ratio of TM1 and TM3 is proved to be the best one.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013)
Series
Advances in Intelligent Systems Research
Publication Date
August 2013
ISBN
10.2991/rsete.2013.186
ISSN
1951-6851
DOI
10.2991/rsete.2013.186How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Ruan Kun
AU  - Zhu Shouxian
AU  - Zhang Xiaodong
AU  - Zeng Wenhua
PY  - 2013/08
DA  - 2013/08
TI  - Water Depth Inversion in Haikou Bay Based on TM Image
BT  - Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013)
PB  - Atlantis Press
SP  - 767
EP  - 769
SN  - 1951-6851
UR  - https://doi.org/10.2991/rsete.2013.186
DO  - 10.2991/rsete.2013.186
ID  - Kun2013/08
ER  -