International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
2. METHODOLOGY
Image- and/or raster-based computation time heavily depends
on image size. To cover a considerably large area, the size of a
QuickBird, IKONOS, or WorldView-2 image becomes huge
multiplied by its multi-spectral bands. On another hand, object-
based image analysis exploits the scale property of objects in
describing the context for segmentation and classification. The
dual-scale processing proposed here takes into account the scale
property not only categorise objects into different levels for
segmentation and classification but also to decide the suitability
level of complexity in processing in order to shorten the
computation time.
The two levels of the main processing are described in the
following sub-sections. It is noted that pre-processing such as
geometric and radiometric correction, pan-sharpening may be
necessary prior to the main processing. Those are easily done by
any remote sensing packages and not repeated here.
2.1 Coarse level
On the coarse level, the simple but robust Statistical Region
Merging (SRM) technique is adopted (Nock and Nielsen, 2004).
SRM starts by sorting the pair of pixels in ascending order of
f (p.p ), expressed as in Eq. 1.
f(p,p')= max
Vaels) Pa Pa
(1
a denotes a spectral channel of the multi-spectral space S
Traversing the above order once and testing for any pair, if the
region R and R’ of the pair are different, R and R’ will be
merged if
IR,-R,|=< JB (R) +b*(R) Na E (s), Q)
where R denotes the average value of region R
b(R)=g
1 nl , with [t| - 0 «1)" "^9,
20|R|\ à
g denotes the number of grey value,
Q is tuning parameter, ö = sl
3t
/| and |R| denote entire image and region R size, respectively.
More details of SRM are proved and presented in Nock and
Nielsen's papers (Nock and Nielsen 2004, Nock and Nielsen
2005). To delineate the big objects, the Q value is fixed at 64
whereas the g value is fixed to 256 so it is required to scale the
simplified image grey value to 0-255. SRM is conventionally
working on pixels that may take long time. To assist speeding
up the conditional search based upon Eq. 2, the initial clusters
are generated via K-mean clustering and morphological filtering
(Vincent 1993). The filtering is to remove the small
meaningless objects, less than 25 pixels, and is applied onto the
spectral clustered images derived by K-mean. K-mean
clustering helps to initially group the pixels using their
multispectral signatures globally. The locally grouping is then
controlled by SRM. After filtering, the pixels in a neighbour
that have similar grey value (class number) will be merged into
a cluster. Those clusters will play as the basic entity for SRM
instead of pixels.
Subsequently, the rule-based analysis is carried out to decide
which objects should be go further to fine level processing as
well as remove irrelevant objects such as water and vegetation.
66
First, homogeneous texture is measured, those with low
homogeneous value is subject to go further. To discriminate
water, vegetation from impervious surface, the brightness and
greenness indices (Yarbrough et al. 2005) in combination with
NDVI are employed. High level of greenness indicates the
vegetation cover after confirmed with high NDVI value,
whereas both low brightness and greenness values indicate the
water body. The impervious surfaces have low greenness and
high brightness values. The outcomes of coarse level
processing, therefore, include the following types:
homogeneous irrelevant objects such as water and vegetation,
homogenous big impervious surfaces objects, and
heterogeneous objects to go further to fine level processing. The
very low homogeneous objects with low brightness and
greenness values would be a cue to focus in mapping the
damages. The whole process is illustrated in Figure 1.
Original
image
K-mean clustering (pixel-based)
Morphological filter
Statistical Region Merging
irrelevant
Rule-based analysis objects
Extracted big Boundaries for
objects fine processing
Figure 1. Coarse level processing
2.2 Fine level
In the detailed processing, a considerably large area has been
masked out on coarse level with expectation that the time
processing will be shortened. The flowchart of this fine level
processing is shown in Figure 2. Going into details, it is
possible to reveal the geometrical beside the spectral properties
of each object. The previously developed non-linear scale space
transformation by the author (Vu and Ban 2010) is adopted here
to derive the morphological profile and form the fine objects.
Again, to speed up the processing, morphological profile is
generated on only the first component of PCA.
Briefly, morphological filtering with reconstruction with
increasing template size is applied onto the first component
analysis. The morphological profiles across the scale space
provide the cues to group nearby pixels to a cluster via
similarity measurement. It also helps to group the clusters with
similarity profile to the same class. Since it works with only
small objects, a limited number of sizes of increasing step of 1
can be used to generate the scale-space, named the scale range.
Granulometry analysis is also employed to ignore the
unnecessary sizes at which no significant changes on the
granulometry spectrum within the scale range.
In addition, the compactness and elongated shape indices
(Bogaert et al. 2000), ratio between object area and object
perimeter/length, are exploited to discriminate building and road
objects. The rest of spectral information is added back via a
simple K-mean pixel-based classification. The spectral class of
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