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ICSGRC.2017.8070568

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2017 IEEE 8th Control and System Graduate Research Colloquium (ICSGRC 2017), 4 - 5 August 2017, Shah Alam, Malaysia
Shape Characterization of Land Covers using SuperResolution Mapping
Anuar M. Muad
Department of Electrical, Electronic and Systems Engineering
Faculty of Engineering and Built Environment
Universiti Kebangsaan Malaysia
43600 UKM Bangi, Selangor, Malaysia
anuar_muad@ukm.edu.my
system [16], geostatistical [17], and spatial regularization
model [18].
Abstract—This paper presents a representation of land cover
from a popular low spatial resolution of remote sensing image,
MODIS 250 m. The spatial resolution of the MODIS image is
enhanced using super-resolution mapping to a resolution that is
equal to resolution of Landsat ETM+, which is 30 m. Two superresolution mapping techniques, Hopfield neural network and
pixel swapping are used to represent the land covers as patch
objects. Parameters for both techniques are varies to investigate
their impact towards the characterization of the object in a single
MODIS image and also in a time-series MODIS images.
This paper investigates the impact of shape
characterization from land cover represented by SRM. Here,
the HNN and PS were used on a single low spatial resolution
of MODIS 250 m and a series of MODIS images.
II. HOPFIELD NEURAL NETWORK
Hopfield neural network (HNN) is an optimization
technique represented as an energy function. The energy
function that represents the problem of SRM comprised of two
parts: a goal function and a constraint function [8], as defined
as


E  wGG  x   wA A  x  
where G(.) is a goal function, A(.) is an area constraint
function, wG is a weight coefficient for the goal function and
wA is a weight coefficient for the constraint function. The goal
function emphasises spatial correlation between sub-pixels
while the constraint function preserves class proportion in a
pixel of a soft classified image [19]. The network comprises a
set of neurons in a two dimensional arrangement in which
neuron (i,j) corresponds to sub-pixel (i,j). In this paper, HNN
with wA > wG, is referred as HNN(A), while when the weights
are equal, wA = wG, the HNN is referred as HNN(E). To exploit
the advantages of the HNN(A) and HNN(E), a technique
known as HNN2 [20] is used.
Keywords—Remote sensing; Hopfield neural network; pixel
swapping; object based remote sensing;MODIS; Landsat
I. INTRODUCTION
Land cover is one of the most fundamental variable for range
of studies about the surface of the earth. Land cover can be
treated as an object in order to study their size, shape,
boundary, and orientation. Accurate object characterization is
essential to estimate the impact of land cover and land uses in
one region, such as biodiversity, urbanization, vegetation, and
many others. Remote sensing has been considered as the most
practical method to acquire information about land cover [1-3].
Although remote sensing is widely used as a source of land
cover information there are many factors that limit the accuracy
of derived land cover information [4] especially the spatial
resolution of the data. Nevertheless, increasing the spatial
resolution of the imaging sensor would increases the cost of the
device. This factor hugely affects the price of purchasing high
spatial resolution imagery from remote sensing agencies. On
contrary, low spatial resolution imagery is cheaper than high
spatial resolution imagery capturing on the same area [5].
However, a major drawback of the low spatial resolution
imagery is that the information is less detail than the high
spatial resolution imagery [6]. Traditional hard classifiers [7]
are unable to map the land cover at a subpixel scale of the low
spatial resolution image.
III. PIXEL SWAPPING
Pixel swapping (PS) is a technique that calculates the
attractiveness of each sub-pixel to a particular class of land
cover in a low spatial resolution image [12]. This technique
starts after an image has been soft classified. Pixel swapping
uses an attractive function to measure an attractiveness level of
a sub-pixel i related to its neighbours j = 1,…,J as given in
Equation 2 that measures the summation of weighed distance
between a sub-pixel of interest and its neighbours.
To enhance the value of low spatial resolution, superresolution mapping (SRM) technique is used. There are many
variation of SRM techniques, such as Hopfield neural network
(HNN) [8-11], pixel swapping (PS) [12,13], back-propagation
neural network [14], Markov random field [15], multi agent
978-1-5386-0380-2/17/$31.00 ©2017 IEEE


J
Fi  x i     ij
j 1
x  
j

where z(xj) is the binary class of the jth sub-pixel at location xj ,
and ij is a non-linear distance weighted function. In this paper,
57

2017 IEEE 8th Control and System Graduate Research Colloquium (ICSGRC 2017), 4 - 5 August 2017, Shah Alam, Malaysia
PS with one layer of neighbour is referred as PS(1), while for
five layers of neighbour is referred as PS(5).
IV. IMPLEMENTATION
The study area covers approximately a 26 km × 28 km area
located in Ontario province, Canada. It is situated between
latitudes 54o26’24”N and 54o12’10”N and between longitudes
85o26’15”W and 85o01’22”W. The area contains huge number
of lakes of varying size and shape. Two sets of image data
were used. First, a time series of low spatial resolution imagery
of MODIS 250 m was acquired between 13 June 2002 and 19
August 2002. Only near infrared band (0.841-0.876 µm) was
used because land and water are highly contrast in this
spectrum. There were 14 MODIS images in the series. The
MODIS images were provided by the USGS Land Processes
Distributed Active Archive Center (LP DAAC) website
(https://lpdaac.usgs.gov/). An example of a MODIS image
acquired on 5 July 2002 is shown in Fig. 1a. The MODIS
images were fused using a phase correlation technique [21,22].
Second, a Landsat ETM+ of 30 m of the region acquired on 10
July 2002 was used to provide ground data as shown in Fig. 1b.
Only the near infrared band image (0.750-0.900 µm) was used.
The Landsat image was provided by the U.S Geological Survey
(USGS)
Global
Visualization
Viewer
website
(http://glovis.usgs.gov/). The date of the Landsat image lies in
the middle of the MODIS images. The MODIS image acquired
on 5 July 2002 was used as a reference image for time series
image because its date of acquisition is the closest to the date
of Landsat. Fig. 2 shows the position of the reference and
ground data images.
Fig. 2. Temporal coverage of a time-series daily MODIS 250 m. A MODIS
image acquired on 5 July 2002 was used as a reference image for a time series
image registration. Landsat image acquired on 10 July 2002 was used as a
ground data as a means to set the orientation of the MODIS image correctly
V. RESULTS
The shape of objects can be characterized by area,
perimeter, compactness, length and orientation. The
compactness of an object is calculated by = 4A/P2, where A
is area and P is perimeter. The index ranges from 0 to 1, where
values approaching 1 indicate that the shape is circular while
values approaching 0 are for linear shape. The evaluation of the
positional accuracy compares the boundary of the represented
objects with the boundary of the corresponding objects from
the ground data. To generate boundary position, the boundary
of the objects was vectorised. The vector difference of the
boundary positions between the represented objects and the
ground data was expressed in root mean squared error (RMSE),
which provides a statistical measure of the positional accuracy
of objects.
The spatial resolution of the MODIS images was enhanced
from 250 m to 30 m, which is equal to the spatial resolution of
the Landsat image. The enhancement process decomposed a
pixel into a number of sub-pixels. In this work, there were
872×936 sub-pixels that eventually become the neurons for the
HNN that was arranged in a layer of two dimensional. The
HNNs were calculated numerically for 10,000 iterations For
the PS technique, these sub-pixels were swapped to produce a
representation of land cover.
(a)
In this work, the lakes were treated as objects. Fig. 3 shows
30 lakes of varying sizes and shapes that were selected and
annotated from the ground data. Corresponding lakes in the
images of land cover mapping derived from different
techniques were compared with the ground data.
(b)
Fig. 1. Datasets (a) one of the MODIS 250 m near IR images acquired on 5
July 2002 and (b) Landsat ETM+ 30 m near IR image taken on 10 July 2002
that was used as ground data.
Fig. 3. Selected lakes used for object based analysis
58
2017 IEEE 8th Control and System Graduate Research Colloquium (ICSGRC 2017), 4 - 5 August 2017, Shah Alam, Malaysia
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
(i)
(j)
(k)
(l)
Fig. 4. Boundary fitting for different techniques on Lake 25. The red line indicates the boundary of the lake from the ground data image, and the blue line
indicates the boundary of the represented lake. (a) Ground data.(b) Hard classifier. (c) PS(1) on 1 image. (d) PS(5) on 1 image. (e) HNN(E) on 1 image. (f)
HNN(A) on 1 image. (g) HNN2 on 1 image. (h) PS(1) on fused image. (i) PS(1) on fused image. (j) HNN(E) on fused image. (k) HNN(A) on fused image. (l)
HNN2 on fused image.
Table 1. Average difference of area (km2) for 30 lakes.
Technique
Single
image
Fused
image
HC
PS (1)
PS (5)
HNN
(E)
HNN
(A)
HNN2
0.1696
0.1471
0.2009
0.1992
0.2006
0.1991
0.1336
0.1425
0.1313
0.1973
0.1290
applied on the fused image produced 0.7218 m error. The
HNN(A) that was applied on a fused image produced the
highest error with 1.4164 m. For both cases of the single
MODIS image and the fused image, the HNN(A) tended to
produce high error because the representation of the boundary
of the lakes appear to be irregular, serrated and not smooth.
Table 3 provides an average of the difference object
compactness compared to the ground data. For the
compactness measurement, the PS(5) applied on the fused
image produce the lowest error with the average error was
0.1858, while the HNN(E) applied on the fused image
produced 0.1932 error and the HNN2 applied on the fused
image produced 0.1961 error. The PS(1) applied on a fused
image produced the highest error with the average error was
0.2460. This scenario suggested that in comparison with other
techniques, the shape of the lakes represented by the PS(5) and
HNN(E) produced the closest appearance to the corresponding
lakes in the ground data.
Table 2. Average difference of perimeter (m) for 30 lakes.
Technique
Single
image
Fused
image
HC
PS (1)
PS (5)
HNN
(E)
HNN
(A)
HNN2
1.1223
0.9910
1.0981
1.1394
1.3259
1.1394
1.1766
0.6732
0.7354
1.4164
0.7218
Table 3. Average difference of compactness for 30 lakes.
Technique
Single
image
Fused
image
HC
PS (1)
PS (5)
HNN
(E)
HNN
(A)
HNN2
0.2362
0.2023
0.2089
0.2289
0.2307
0.2257
0.2460
0.1858
0.1932
0.2397
0.1961
To evaluate the positional accuracy, the shoreline of the
represented lakes produced from different land cover mapping
techniques was compared with the boundary of the
corresponding lakes in the ground data image. Several points
along the shoreline of a represented lake were selected. These
points were vectorised and compared with the closest points
along the shoreline of the corresponding lake in the ground
data. As an example, Fig. 4 shows boundary fitting for
different land cover mapping techniques on Lake 25 from the
image in Fig. 3. The boundary of the represented lakes was
overlaid with the boundary of the corresponding lake in the
ground data.
Fig. 4 shows that object represented by traditional HC have
an unrealistic jagged and pixelated. Using only a single
MODIS image, the boundaries represented all the HNN and PS
techniques appeared to be irregular and serrated. However, the
Table 1 provides an average of the difference for area (in
km2) compared to the ground data. Overall, the proposed
HNN2 applied on the fused MODIS time series images
produced the lowest error for the area measurement with the
average error was 0.1290 km2, while the PS(5) applied on a
single MODIS image produced the highest error with 0.2009
km2. By using the fused image, all the techniques
demonstrated decreasing in the error for the area measurement
of the lakes. Table 2 provides an average of the difference for
perimeter (in meter) compared to the ground data. For the
perimeter measurement, the PS(5) applied on the fused image
produce the lowest error with 0.6732 m, while the HNN2
59
2017 IEEE 8th Control and System Graduate Research Colloquium (ICSGRC 2017), 4 - 5 August 2017, Shah Alam, Malaysia
application of the time series MODIS images produced
different results than by using a single MODIS image. For the
time-series MODIS images, the lake boundaries in the HNN(E)
tended to be smoother and less irregular when compared to the
lake boundaries in the HNN(A). This trend is not apparent in
the single MODIS image case in which both the HNN(E) and
the HNN(A) produced irregular lake boundaries. The
advantage of the HNN(E) in retaining the smoothness of lake
boundaries and the advantage of the HNN(A) in representing
small lakes were exploited by combining the these two
techniques in the proposed HNN2.
lakes represented appeared to be blocky and jagged. The
positional accuracy for the represented lakes in the fused image
was higher than that in the single image. The boundary of the
represented lakes in the fused image appears to be smoother
and less irregular than the represented lakes in the single
image. An example of this scenario is shown in Figure 4.
VI. CONCLUSION
This paper presents the application of different SRM
techniques on MODIS 250 m imagery. To characterize the
shape of the lakes, standard measures such as area, perimeter
and compactness were used. In general, the HNN2 technique
applied on a time series MODIS images was more accurate
than the other techniques in predicting the area of the
represented lakes, while the PS(5) was more accurate in
predicting the perimeter and the compactness of the
represented lakes. On the positional accuracy, the HNN(E) was
more accurate than the other techniques as the boundary of the
lakes represented was smooth, which resemblance the
boundary of the lakes in the ground data. As a conclusion, all
the SRM techniques applied on a time series MODIS images
produced better accuracy than when applied on a single
MODIS image.
The vector difference between the points along the
boundary of the represented lakes and the points along the
boundary of the corresponding lakes in the ground data was
measured to produce positional accuracy information, as shown
in an example in Fig. 5. The root mean squared error (RMSE)
of the position error of the selected 30 lakes (Fig. 3) was
calculated and the result is presented in Table 4. In general,
the HNN(E) applied on the fused image produced the highest
positional accuracy compared with other techniques with the
average RMSE produced was 82.98 m. The proposed HNN 2
technique that was applied on the fused image produced the
second highest positional accuracy with the average RMSE
was 85.00 m. The HNN(A) applied on a single image
produced the lowest positional accuracy with the average of
the RMSE was 145.93 m.
ACKNOWLEDGMENT
Funding by the Ministry of Higher Education Malaysia under
the research grant FRGS/2/2013/TK03/UKM/02/5 is gratefully
acknowledged.
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60
2017 IEEE 8th Control and System Graduate Research Colloquium (ICSGRC 2017), 4 - 5 August 2017, Shah Alam, Malaysia
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