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The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics
Chen, Jun

ISPRS, Vol.34, Part 2W2, "Dynamic and Multi-Dimensional GIS’’, Bangkok, May 23-25, 2001
Fengrong HUANG
Institute of Nautical Technology, Dalian Maritime Univ., Dalian 116026,China Tel:(86)-411-2664232
Fax: (86)-411-2664232
E-mail: mellisa001@sina.com)
Key words: oil spill, remote sensing information extraction, hot area sampling, FDL-W
The bands of remote sensing of LANDSAT and NOAA is not ideal for catching Sea Spilled-Oil data, which results in the harder on study
of monitoring the Ocean environment pollution though the spectrum data of Spilled Oil in sea proves the possibility of extracting the
Spilled-Oil information from TM and AVHRR image. This paper introduced the method of Feature-Data-Line-Window (FDL-W) to auto
extract the Sea Oil information from TM and AVHRR images.
Generally, the accidents of the perils of the sea may cause the
pollution of spilled oil, which is paroxysmal. Therefore the
prerequisite to response the accident of Spilled Oil is whether
we can accurately extract, in a short period of time, the
information of affected region, i.e. range, area, the quantity of
the spilled oil etc. So the auto-extraction of the Spilled-oil
information from Satellite Image is the key issue we are very
concerned about. Because of the development of the Science
and Technology of Remote Sensing, quickly update the
Remote Sensing data is no longer the problem. How to solve
the issue of quantity of Remote Sensing is more important than
ever before. In this paper, we would like to discuss the thought
of the quantity of Remote Sensing base on the model of
information extraction of oil spills from Remote Sensing Data.
1. Introduction
The method of Featured-Data-Line- Window (FDL-W) is
based on the oil spectrum data. According to the variety of oil
spills from measuring on Sea Surface Experiment, we got the
spectral characteristic graph of five kinds of oil film and
seawater. Then by processing the history data of oil spills
using the multi-band linear enhancement and composition, we
got the thematic imagine of oil spills information and selected
the hot spot area. Afterwards we synchro-sampled the hot spot
area for all bands of TM or AVHRR data, established the oil-
spilled gray-level database of all bands, drawn the oil-spilled
FDL. For the relation of spectrum and thickness of the film, we
adopted the each FDL-Window as a operator to compare with
the each pixel-gray on the whole imagine one by one and band
over band. A computer program controls this procedure. If the
pixel-gray of each band is all contained within their Window the
pixel will pick up and record, otherwise it will be canceled. So
at first we must get all the spectrum data and spot image
feature of all kinds of spilled-oil, and find the spectral bands of
spilled-oil film by special image processing system. Then build
a hot-spot area and take a synchronous sample from all
channels in this area to set up a spilled-oil image Grey
intensity database. Finally, make s model of the correlation
between spectrum value of spilled-oil and the thickness of oil
film by analyzing spectrum data of spilled-oil. And use
distinguishing model technology to find out discriminating
function, then calculate spot image of all channels to extract
the information.
2. The Spectrum Feature and Image gray of Spilled-oil
The sea spilled-oil spectrum data measuring on Sea Surface
show that after seawater is polluted by spilled-oil its reflectivity
will have a great change. Usually there is a drop tendency in
the visible light section whose reflectivity is 0.005-0.033 and
whose peak value is near 0.55-0.57. The reflectivity in red light
and reflected infrared section (0.7 -1.040mm) is also low. It’s
often less than 0.01, but most of it is less than 0.005. The