A Hyperspectral Based Multisensor System for Marine
Oil Spill Detection, Analysis and Tracking
F. López-Peña and R. J. Duro
Grupo Integrado de Ingeniería, Universidade da Coruña, Spain
flop@udc.es, richard@udc.es
Abstract. In this work we present a new multisensor system which is being
currently designed and developed within the framework of a research project
founded by the Spanish Ministry of Science and Technology. This system is
designed to address the detection of oil, its characterization, and the tracking of
oil spills on seawater by means of hyperspectral image segmentation. The
system core is an instrument platform combining a hyperspectrometer with a
series of positioning sensors for the determination of its geographical position
and orientation. The system is completed with GIS and hyperspectral image
analysis subsystems.
1 Introduction
Massive marine oil spills due to accidents as well as illegal dumping of vessel waste
are becoming serious social and environmental problems all around the world and are
causing serious damage to fauna, flora, fisheries and tourism, as well as potential
public health consequences. The pollution of the marine, coastal and harbour
environments by oil products is becoming an increasingly important issue around the
globe [1] [2] [3]. Early detection and subsequent accurate tracking of the spills have
proved to be of crucial importance in the prevention and mitigation of these problems.
Thus, remote detection and observation of oil spills on the sea have been tackled in
the last few decades using various optical and Radar techniques and by means of both
airborne and space-borne platforms. A comprehensive survey of the different systems
and methodologies used for this purpose can be found in Fingas and Brown [4]. In
this work the authors argue that current technologies do not support the claim stated
recently which suggests that satellite remote sensing could replace airborne remote
sensing for this use. They also indicate that at the present time and for the foreseeable
future, there is no single sensor able to provide all the information required to detect,
classify, and quantify oil in the marine and coastal environment. These assertions are
supported by other authors, such as Glackin, & Peltzer [5]. In the first chapter of their
book these authors make the point that airborne remote sensing would prevail over
space-borne sensors for applications requiring some combination of great spatial
resolution, cost effectiveness, product quality, refresh rate, and timeliness of product
delivery to the user.
A common trend in remote sensing of the earth points towards combined advances
in sensor technologies and image and data processing. In the near future an
international proliferation of remote sensing devices and systems as well as their
increasing commercialization is expected, this will require a greater number of highresolution systems with lightweight instruments for application in small satellites and
light planes, and a reduction of costs. High resolution hyperspectrometers are among
the instruments that will be used in an ever increasing number, particularly when
applying remote sensing to oil spill mitigation. [4][6][7]
2 Project’s statement
The aim of the present project is to develop a complete, automatic, flexible, reliable,
fast and precise real time detection and cartographic representation system for marine
spills. It is going to be based on a lightweight hyperspectrometer to be used from light
aircraft and, later on, from unmanned aerial vehicles. The main characteristics of the
system are:
1. Real time tracking of spills in marine environments.
2. Determination and mapping of the affected areas in coast and inlets.
a. Visible coastal areas (dry ground).
b. Inlets and shallow water areas with layers of contamination in the
bottom.
c. Sandy areas where the spill is covered by layers of sand.
3. Study of the possibility of remotely tracking certain parameters in the
ecosystems in order to monitor their recovery.
Consequently, to fulfill all these project’s requirements it is necessary to create a
hyperspectrometric data sensing and analysis system that is appropriate for the task in
hand, for that purpose it is required to jointly and simultaneously address different
research lines. Firstly we have two research lines concerning, on one hand, the
conception and construction of the sensor itself and, on the other, the system that must
accompany it in field measurements, so that through sensor fusion techniques the
reliability of the results increases and real time transmission and processing permit a
fluid operation. A third line has to do with all the atmospheric and illumination
modeling processes as well as the integration of geolocalization and fused sensor
information in order to generate spill maps through the combination of different
images. Finally, there is a line concerning the complex processing of the spatial and
spectral information contained in the images allowing the detection of the spills and
their discrimination from other types of materials present in them. This discrimination
process should take place in real time for spill tracking, which requires the
development of distributed classification techniques and algorithms that must be very
precise and robust for generating spill maps, requiring its integration with a solid GIS
cartographic system. The classifiers should integrate the information from different
sensors and should be capable of contemplating different illumination and
atmospheric situations, which could vary even during the same sensing flight. Our
main task is the control of spills of a known material (i.e. oil) but we are also
interested in monitoring the evolution of the environment in order to activate alarms
when infrequent materials appear. For our main task the aim is to obtain specialized
classifiers or detectors from the knowledge of the spectral features of the materials
(based on samples or on formal scientific knowledge). In the second case, the aim is
to build spectral identification mechanisms able to discover in an unsupervised
manner significant variations in the environment that cannot be anticipated when no
previous spectral information is available.
An ultimate target of our project is to promote the development of an Oil Spill
Risk Management System for the Atlantic coast of Spain. In this sense, its
classification and GIS systems, as well as their corresponding databases, should be
open to the integration of data coming from other remote sensing sources.
3 Hyperespectrometer and sensorial fusion system
The core of the system is a hyperspectrometer that needs to be fused with other
sensors. The project requires a light, compact and low cost hyperspectrometer, and as
existent commercial solutions are too big and weighty for that role, the design and
construction of one of these instruments is included among the project tasks.
According with the basic requirements expressed above, the initial conditions
imposed in this instrument’s design are:
- Overall dimensions of 300mm X 200mm X 125 mm.
- Weighting less than 10 Kg.
- Maximum use of standard and commercial components.
In order to accomplish these requirements, the spectral range has been initially
constrained to 400 nm – 1000 nm. The system is going to be modular enough to
accept later on, if an extended range is required, analogous modules in different
spectral ranges. Two different instrument’s prototypes using reflexive and refractive
optics respectively are currently under development. The final decision about
choosing one or the other, or even a mixture of both, will be taken only after their
construction and testing processes.
The hyperspectrometer output is going to be fused with the contributions from
diverse sensors. The platform to fuse these signals is being developed within this
project’s frame. It is conceived as low-cost real-time modular platform having an
open architecture, in the sense that it is going to be easy to add/de-install/replace
sensors. Initially the sensors used are going to be a GPS to track the position and three
minigyroscopes and three accelerometers to track 3-axis rotations of the carrier
vehicle. A more detailed description of the modular architecture of this platform is
presented by Gil-Castiñeira et al [8].
The sensorial platform will carry out the task of stabilizing the hyperspectrometer
while remote sensing is performed. It will also take care of the synchronization of the
hyperspectrometer’s front rotating mirror taking into account the carrier’s altitude and
velocity.
4 Hyperspectral segmentation system
Regarding the segmentation system, two different approaches must be studied. On
one hand, when there is no knowledge on the endmembers present in the image, a
unsupervised classification and endmember extraction system has to be applied. This
system is being developed by one of the groups participating in the project and is
studied in detail elsewhere [9]. On the other, when the endmembers, or at least part of
them, are known, it is necessary to introduce a segmentation system that produces as a
result of the processing of the spectral information of each pixel the abundance of
each endmember in that location so as to allow a mapping of the areas where the spill
in question is located. We have developed and constructed a multi and hyperspectral
image segmentation system made up of Gaussian synapse based networks that has
been successfully used to perform precise and effective segmentation of hyperspectral
remote sensing images. A full description of this system and a discussion on its
performance can be found in Crespo et al [10]. The segmentation system we have
constructed has been developed for general detection purposes and consists of a set of
Gaussian synapse based networks working in parallel over the spectral dimension of
each pixel of the image. These detectors produce a detection probability surface
associated with the category they have been trained for, that is, an abundance
indication. Obviously, a pixel may be assigned a detection probability by two or more
detectors. This may be due to several causes: non discriminant training sets, very
similar spectra, mixtures of categories within the same pixel (take into account that
depending on the altitude of the flight and the spatial resolution of the instrument a
pixel may represent very large areas), noise, etc. Thus, after going through the
detectors, each pixel is characterized by a detection probability vector and the way
this detection vector is used will depend on the application. Consequently, to decide
on the final label assigned to the pixel, all the detectors send their information to a
final decision module. The final decision will be made depending on the desires of the
user. For instance, the decision module may be trained to choose the most likely
category for the pixel or to assign combinations of detections to new categories so
that the final image indicates where there is doubt or even prioritize some types of
detections when searching for particular objectives such as oil.
Our multi and hyperspectral image segmentation system using Gaussian synapse
based networks has been proven to be very accurate and effective when applied to the
analysis of several benchmark multi a hyperspectral ground images. Unfortunately,
none of the available images of these types correspond to marine spills. A particularly
difficult remote sensing case of oil spills is when it starts to disperse; its natural
degradation on the sea surface at first produces an increase of its viscosity, then
floating oil fragments into innumerable scraps of ever-decreasing size. Therefore, we
have sought data sets that contemplated the same problems we are facing here, that is,
detection of minoritary elements in the pixels (elements whose cover area is less than
5% of the area corresponding to the pixel) and image areas where mixtures of
elements are present in different proportions. To this end we have considered two
types of data sets. On one hand, we made use of a data set derived from a synthetic
image that contained different combinations of endmembers in controlled proportions,
so that the results of the application of the networks could be validated with well
known data. On the other, we took portions of a well known real benchmark image
where the type of problem we are contemplating is present. The spectra used for the
latter correspond to one of the benchmark images available in the literature, in
particular to the Indian Pines 1992 image [11] obtained by the Airborne
Visible/Infrared Imaging Spectrometer (AVIRIS) developed by NASA JPL which has
224 contiguous spectral channels covering a spectral region form 0.4 to 2.5 m in 10
Fig 11: Spectra for three lines of pixels crossing a boundaries between two zones (right) and the colour coded result produced by the
egmenter described above. Under each color coded result we display the detection values provided by each detector at each pixel in
e line (the graphs and the enlarged pixel areas are aligned). To the right of each enlarged area we display the spectra corresponding to
all the pixels in the line. It is easy to see that the border pixels, which contain mixtures are labelled as mixtures.
Fig. 1.Transition pixels selected in between two different endmembers
nm steps. It is a 145 by 145 pixel image with 220 spectral bands that contains a
distribution of two-thirds of agricultural land and one-third of forest and other
elements (two highways, a rail line and some houses and smaller roads). The ground
truth available for this image [11] designates 16 not mutually exclusive classes.
To verify how well Gaussian Synapse based networks are adapted to the task of
discriminating the presence of those minoritary elements in the pixels and provide an
indication of the components and proportions that make up the pixel, we have
extracted from the Indian Pines image groups of pixels representing transitions
between areas with different labels in the hope that the intermediate pixels contain
mixtures of elements from both sides of the transition. As no ground truth was
available on the proportions of elements present on these pixels we also generated
artificial sets of pixels starting from the same endmembers and simulating the same
type of brusque transition so as to provide a validation set for the results obtained
from the real image. The results from running the detectors generated before over the
transition areas selected can be seen in figure 1. These data clearly display a transition
between categories where two or three pixels in the middle contain mixtures of the
elements present on the sides.
As commented before, to check the validity of these results, we generated a synthetic
1.2
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NATURAL IMAGE
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GRASS PASTURE
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Fig. 2. Behavior of the detectors of two given endmembers through a brusque transition for a
series of pixels obtained from the Indian Pines image (left) and for the synthetically generated
pixels (right).
set of pixels through the transitions with known proportions and compared them to the
real ones obtained from the image in terms of the proportions produced by the
Gaussian Synapse based networks. The evolution of the proportion of endmembers
for these two cases along the pixel line can be observed in figure 2. These results are
quite encouraging, as the detection on the real image closely follows that of the
synthetic one with well known proportions.
To better decide how accurately these networks could predict proportions, and detect
minoritary elements we generated a new transition in which the evolution of
proportions from one endmember to the other is linear. After passing the detectors
over this line, we obtained the results shown in figure 3 where we display the
percentage of a given endmember (solid line) present in each pixel and the values
provided by this endmember detector. It can be observed that these values agree quite
closely and that low percentages of the endmember in the image are easily detected.
These results show that our segmentation system is able to detect endmembers when
their proportions are as small as 3% of the pixel surface. This performance is very
good and supports the idea of applying the technique in detecting sparse bits of oil.
1
0.9
Value provided by network
0.8
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Real Proportion of Grass-Pasture
Fig. 3. Prediction by Gaussian Synapse Networks of the percentage of an endmember
present in synthetically generated pixels as compared to the real values. Perfect results are
represented by the black line.
5 Conclusions
A new multisensor system being developed to perform marine oil spill detection,
analysis and tracking is presented. The system is based on a hyperspetrometer
integrated in a modular platform for sensor fusion. The processing of the information
when the endmembers are known, that is, the segmentation is carried out by using a
technique based on a new type of Artificial Neural Networks containing Gaussian
synapse. This approach allows the detection of the presence of materials when they
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cover less than 5% of the area of a pixel, which is quite important in the case of oil
spills, due to their tendency to break up into small oil patches. The system has a low
cost and is compact and lightweight, allowing it to be used from light airplanes and
unmanned aerial vehicles.
Acknowledgements
This work is supported by the MCYT of Spain (TIC2000-0739C0404 and VEM200320088-C04-01), and NATO (PST.CLG.978744).
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