Paper Title (use style: paper title)
Assessment of the Grey-Level Co-Occurrence Matrix for Land Cover
Classification using Multi-spectral UAV imageThanh Tung Do1, Tien
Yin Chou21Master in Urban Planning and Spatial Information, Feng
Chia University, 100 Wenhwa Rd, Situn Dist., Taichung 40724, Taiwan
R.O.C, [email protected] 2GIS Research Center, Feng Chia University,
100 Wenhwa Rd, Situn Dist., Taichung 40724, Taiwan R.O.C,
[email protected]
ABSTRACTTexture features based on the grey-level co-occurrence
matrix method are extracted from an UAV near-infrared image by
using four second-order statistic, eight window sizes, and two
quantization levels. The four UAV multi-spectral bands are combined
with each textural band individually and with all four textural
bands. From these combination, a supervised classification method
based on the maximum-likelihood algorithm is chosen to classify the
land cover into five classes. The classification accuracy is
measured by kappa coefficients calculated from confusion matrices.
The results show that the addition of texture features to the
spectral image provides a significant improvement in the
classification accuracy of each land cover type as compared with
the classification obtained from the spectral image only.
2015 ICEO&SI and ICLEI Resilience Forum , JUNE 28-30,
Kaohsiung. TaiwanPAPER No.
INTRODUCTIONThe application of Unmanned Aerial Vehicle (UAV) has
increased considerably in recent years due to their greater
availability and the miniaturization of sensors, GPS, inertial
measurement units, and the other [7]. The advantages of UAV
compared to manned aircraft systems are that UAV can be used in
high risk situation without endangering a human life and
inaccessible areas, at low altitude and at flight profiles close to
the objects where manned systems cannot be flown [1]. Furthermore,
in cloudy and drizzly weather condition, the data acquisition with
UAV is still possible, when the distance to the object permits
flying below the clouds. Moreover, supplementary advantages are the
real-time capability and the ability for fast data acquisition,
while transmitting the image, video and data in real time to the
ground station.With very high spatial resolution (0.14 x 0.14m) and
multispectral bands (R-G-B-NIR), the level of detail present in the
UAV image has increased considerably when compared to the other
multispectral satellite images. For visual interpretation, a finer
spatial resolution permits better land cover discrimination.
However, the increased amount of detail creates new problems for
information extraction using automated classification techniques
[3]. The finer spatial resolution increases the
spectral-radiometric variation of land cover types.There are two
major approaches to tackle that problems in relation to the
increased internal variance. The first is applying mathematical
transformation to the original spectral data to remove the excess
spectral information. The second approach considers the internal
spectral variance of classes as a valuable information that can be
used as an additional information in characterizing and identifying
land covers.Spectral, textural and contextual features are three
fundamental pattern elements used in human interpretation of color
photographs. Spectral features describe the average tonal variation
in various bands of the visible and/or infrared portion of an
electromagnetic spectrum, whereas textural contain information
about the spatial distribution of tonal variations within a band.
Contextual features contain information derived from blocks of
image data surrounding the area being analyzed. When small image
areas from black and white images are independently processed by a
machine, then textures are most important [2].Texture is an
important characteristic for the analysis of many types of images.
It presents the first level of spatial properties that can be
extracted from an image. It can define as the relationships between
grey levels in neighboring pixels which contribute to the entire
appearance of the image. In statistical texture analysis, texture
features are extracted from the statistical distribution of
observed combination of intensities at specified positions relative
to each other in the image. According to the number of intensity
points (pixels) in each combination, statistics are classified into
first-order, second-order and higher-order statistics. The Grey
Level Co-occurrence Matrix (GLCM) is one of the most popular
methods to extract second order statistical texture features. Third
and higher order textures consider the relationships among three or
more pixels. These are theoretically possible but not commonly
implemented due to calculation time and interpretation
difficulty.The GLCM contains the relative frequencies with which
two neighboring pixels occur on the image, one with grey level i,
and the other with grey level j. Several statistical measures, such
as contrast, entropy, and angular second moment can be estimated
from the GLCM to describe specific textural features of the image
[2]. Each textural feature can be used to create a new texture
image/band which can combine with original spectral feature/band
for classification.When classifying the regions of an image by
using GLCM method, there are several factors to consider: the
spectral band, the quantization level of the image, the moving
window size, the distance and angle for co-occurrence computation
and the statistics used as texture measures.In this study, five
land cover types are classified from original multispectral UAV
image combined with its textural feature/bands to evaluate the
influence of texture features based on GLCM method on the
classification accuracy. Thus, the major objectives of this study
are: i) to evaluate the influence of the window size, the
quantization level, and the statistics used as texture measures on
classification accuracy; ii) to measure the influence of the window
size and the quantization level on extracting the texture
features.METHODOLOGYThe study site is an area located in Zhuoshui
River side, in the Yunlin County, Taiwan (Fig. 1). The UAV image is
acquired in July 2013. This area is a rural area with most of land
cover types related to vegetation and agricultural field.
UAV true-color image of study siteTexture bands extraction and
band combinationsBased on GLCM method, sixty-four texture bands
(Fig. 2) were created by using the original UAV near-infrared band
at a spatial resolution of 0.14 x 0.14m. This spectral band
exhibits better contrast between land cover types than the visible
spectral band (R-G-B band).
Creation of texture band and band combinationThe quantization
level of 16 and 32 were chosen for texture bands creation. Eight
window sizes from 3 x 3 pixels to 41 x 41 pixels also were chosen
for testing. This selection permits to cover some range of the land
category spatial pattern dimension on the UAV image and to assess
the influence of window sizes on classification accuracy.During the
process of co-occurrence matrix computation, the distance between
pixels was kept constant at one. Based on the assumption that no
land cover type exhibit a preferential texture directionality, the
co-occurrence matrix over four main angles (0o, 45o, 90o and 135o)
was averaged. Four second-order statistics were calculated from
co-occurrence matrix including: the contrast (CON), the angular
second moment (ASM), the correlation (COR), and the entropy (ENT)
((1) to (4)).
(1)
(2)
(3)
(4)
where is th entry in a normalized grey-level co-occurrence
matrix; is number of distinct grey-level in quantized image; , , ,
are the means and standard deviation of and ; is ith entry the
marginal probability matrix obtained by summing the rows of [2].The
texture images/bands were normalized on a 256 grey-level scale
using a linear transformation [6].Classification accuracy
assessmentThe four UAV multispectral bands were combined with each
texture image individually and all four texture images together
(Fig. 1). These combinations are classified by using a supervised
classification method based on the maximum likelihood algorithm.
These classifications were repeated for each window size and
quantization. The classification from original UAV multispectral
image also was done for assessing the contribution of texture
features in the discrimination of land cover types. The land cover
classification scheme is including: bare soil, dense vegetation,
agriculture, grassland, and residential areas. Most of the classes
are following the USGS scheme and emphasizing the pattern and
spatial variability of the image [8]. The sample (signatures
collected from the image) areas were selected as the training sites
by using the on-screen digitized features. A total of 250 randomly
sample (50 for each cover type) were chosen by using another kind
of RGB UAV image at a spatial resolution of 0.06 x 0.06m as
reference image. These areas were systematically and proportionally
selected throughout the whole image.To measure the classification
accuracy, the Kappa Coefficient was calculated from confusion
matrices. This coefficient can measure the agreement between
estimated land cover classification and reality land cover or to
determine if the values contained in an error matrix represent a
result significantly better than random [5]. Kappa coefficient is
computed as:
(5)
where N is the total number of site in the matrix, r is the
number of row in the matrix, is the number in row i and column i,
is the total for row i, and is the total for column i [5].To
calculate the agreement between classified and the reference data
for an individual class, the conditional Khat coefficient was
calculated.
(6)
where is the number of observations correctly classified for a
particular category, and N is the total number of observations in
the entire error matrix.RESULT AND DISCUSSIONClassification
accuracy improvement by adding texture bandsThe classification
accuracy calculated from original multispectral UAV image for each
land cover type is low, especially for the bare soil areas (Table
I). The results show that the classification accuracy is
considerably improved when the texture features is added to the
original spectral image. The most significant improvement is in the
classification of grassland (from 69 to 97%), following by bare
soil (from 47 to 73%), agriculture (from 70 to 84%), and
residential areas (from 82 to 97%).Classification accuracy
comparisonCover typesOriginal UAVTexture*featureUAV &
texture
Bare soil0.478 bds-41/160.73
Grassland0.69ENT-25/320.97
Dense Vegetation0.788bds-25/320.87
Agriculture0.70ASM-13/320.84
Residential0.828bds-33/320.97
* 8bds=8 bands (4 spectral bands, 4 texture bands), first number
is window size, second number is quantization levelThe texture
combination that provides the best classification accuracy change
greatly from one cover type to another. For bare soil cover type,
the combination of 4 spectral bands and 4 texture bands provides
the highest classification accuracy. In other hands, the
combination between 4 spectral bands and one second-order statistic
CON provides the best classification accuracy for grassland cover
type.Influence of window size and texture feature on classification
accuracyThe window size is a very important factor that is
responsible for most of the variation in the image classification
process. To evaluate the influence of window size on classification
accuracy, the means (from two quantization levels) of five kappa
coefficients obtained for each cover type were calculated for eight
window sizes (Fig. 3).
Mean kappa coefficient of each land cover type
Discrimination between agriculture and bare soil at window size
of 25 x 25 pixel
Discrimination between grassland and dense vegetation at window
size of 25 x 25 pixelThe results show that the classification
accuracy is changing from one window size to the others. It seems
to exist a window size that maximizes the classification accuracy
for each land cover type. The window size of 25 x 25 pixels can be
seen as the most suitable to obtain accurate classification results
for more than one land cover type. The smaller window sizes do not
show the satisfactory results. These window sizes may not capture
the pattern of most classes. The improvement of the discrimination
between each cover type by adding a texture feature to the spectral
band can be described through the statistics of training data. The
discrimination between agriculture field and bare soil, grassland
and dense vegetation area, grassland and residential areas are
extracted (Fig. 4, 5, and 6). The statistical separability is very
low when using the multispectral bands alone. It is significantly
improved when the texture features added to the original spectral
images. The class separability increases because the unique texture
pattern characterizes each class. In case of several cover types,
the signatures are still overlapping. However, by using
multi-window size of texture features, the separabilty is improved
when compared with the results obtained from the spectral bands
only. The ENT and ASM provide good separabiltiy between
agriculture, bare soil and grass land, while the ENT at window size
of 41 x 41 pixels provide good separability between grassland and
residential areas.
Discrimination between grassland and residential area at window
size of 41 x 41 pixel
Mean grey level values of texture images at quantization level
of 16
Mean grey level values of texture images at quantization level
of 32Influence of quantization level and window size on the texture
features extractionTo evaluate the relationship between window
size, quantization level and the creation of texture features, the
histogram of four texture bands at eight window size and two
quantization level was extracted (Fig. 7 and 8). The observations
show that the trend of texture features extracted from 16 and 32
quantization levels are almost similar and contain basically the
same information. The ENT and CON values increase progressively
with an increase in the window size, whereas the ASM and COR values
decrease with the increasing of window size. The variations of all
four texture images are significant from window size of 3 x 3
pixels to 13 x 13 pixels, but do not vary much over the other
window sizes. The ENT image has the smallest variation, while the
COR image has the highest.CONCLUSIONIn this study, the textural
approach based on GLCM method is using to obtain a significant
improvement in land cover classification from multispectral UAV
image. The classification accuracy is influenced by all three
factor: window size, statistics and quantization level. The
classification accuracy is considerably improved when the texture
features is added to the original spectral image. In further work,
its required to evaluate the influence of variables directly
associated with GLCM method such as inter-pixel angle and
inter-pixel distance on characterizing a particular cover type from
an UAV image. Its also important to extract texture features from
more window size and second order statistics for identifying the
best combinations of spectral and textural image to maximize the
classification accuracy.Acknowledgment We gratefully acknowledge
the funding support and data support from GIS Research Center, Feng
Chia University, Taiwan.ReferencesA. Rango, S. Laliberte, C.Steele,
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