3. RESEARCH PLAN
The chart in Appendix.1 describes the structure of the proposed
research.
3.1 Site Selection
The site selected for this study is Kuwait southern CRE (Kubbar
and Umm Al-Maradem) (Figure 1). The site is selected based
on a number of criteria. In comparison with other CRE around
the world, the selected site represents an extreme environmental
condition due to excessive human activities and natural
environmental impacts (Kuwait Diving Team (KDT), 2009). In
addition, there are few scientific studies implemented in or near
the selected site. This has led to a limited understanding by
experts and policy makers of Kuwait's CRE and how these are
changing over time. The location of the selected site, in the
southern area of Kuwait, allows the site to be accessible to
public, as it is far away from politically restricted areas, which
are mainly located in northern part of Kuwait. The availability
and the cost of resources (field measurement equipment, data
from Al-Hazeem (2007) and satellite imagery) are considered as
a secondary site selection criteria. On these counts the selected
site scores well against other candidate CRE.
Kuwalt Bay
Arabian Gulf
e
Kubbar Coral Reef
mm Al-Maradem Coral Reef]
Figure 1: Map Showing the Location of the Selected Kuwait's
Coral Reef (Kubbar and Umm Al-Maradem (ESRI, 2011)
3.2 Image Selection
From table 2, it can be concluded that Worldview-2 could be a
suitable satellite high spatial resolution sensor for this study as
it has the highest spatial resolution. On the other hand,
Hyperion could be a most suitable satellite sensor as it has the
highest spectral resolution.
Table 2: Comparison of Satellite Images Spectral, Spatial,
Temporal Resolutions and Spectral Range
Bands Resolution (m) Range (um)
0.8
panchromatic Bays
4-
multispectral
-5m
panchromatic
10m
- NÁSA, n.d
- NASA, 1999
-NASA, 2011b
- GeoEye. 2011
-51C, 2010
- USGS. 2010
RUN
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
3.3 Geometric and Radiometric Corrections
Selected images will be geometrically corrected using a number
of techniques, such as pointwise polynomial, piecewise
polynomial and orthorectification correction models. In
addition, the study will conduct a radiometric correction for the
selected images to remove the atmospheric and water column
effects. Atmospheric correction methods, such as Moderate
Resolution Atmospheric Transmittance (MODTRAN) and
Atmospheric and Topographic Correction Model (ATCOR),
will be employed to remove atmospheric attenuation and
scattering. Approximate water column correction methods, such
as those utilized by Lyzenga (1978) and Lee et al (1999), will
be used to remove the effects of water column attenuation,
improve the visual interpretation of imagery and improve
classification accuracy. Furthermore, the spectral reflection of
light from water surface (sun glint) will be removed using
various techniques. For example, Hochberg et al (2003)
presented a new method wherein sun glint component of the
remotely sensed signal is removed from visual wavelength
spectral bands by the utilization of information from a spectral
band in near-infrared (NIR). Image pixels are adjusted to
remove the glint component of the recorded signal, leaving only
the component derived from benthic reflectance and radiative
transfer processes within the water column. These techniques
will potentially demonstrate a substantial visual improvement in
the shallow coral reef ecosystem images, thereby increasing
accuracy and classification.
3.4 Image Classification
Prior to implementing image fusion process, the selected images
will be simply classified using a number of pixel-based digital
image classification methods (supervised and unsupervised
classification) such as the Migrating Means Clustering classifier
(MMC) and the maximum likelihood classifier. This will be
followed by more complex classification method such as object-
oriented classification (OOC).
3.5 Image Fusion
After the selected images are classified into object classes, a
number of image fusion methods such PCA (Principal
Components Analysis), arithmetic combinations, and wavelet
based fusion will be examined to fuse the high spectral
resolution and low spatial resolution image with the low
spectral resolution and high spatial resolution image. The main
objective of the fusion methods will be to create from the
collection of input images a single output image which contains
a better description of the scene that the one provided by any of
the individual input images (Mitchell, 2010).
In addition, a specific fusion model will be developed and tested
to preserve the spectral information of the image. The new
developed method will fuse hyperspectral imagery (possibly
Hyperion) with multispectral imagery (possibly Worldview 2).
When fusing these two images, each band of the high spatial
resolution image (e.g., Coastal Blue band (400-450 nm) of
Worldview 2) will be merged with the multiple spectral of the
high spectral resolution image (e.g. 6 bands between 400-450
nm of Hyperion). The fused image will have a large number of
spectral bands and this will potentially provide more useful and
accurate information. In addition, a new fusion method will be
developed to combine information from different sources based
at the object level. The method will produce all pairs of
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