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International J. of Multidispl.Research & Advcs. in Engg.(IJMRAE),
ISSN 0975-7074, Vol. 8, No. I (April 2016), pp. 1- 15
SUPERVISED AND UNSUPERVISED ALGORITHM FOR LAND
COVER CLASSIFICATION USING SPOT5 IMAGERY
IMZAHIM ABDULKAREEM 1, AKRAM KHLAIF 2
1Asst. Prof, 2 Asst. Lecturer
Abstract
Many methods exist for remote sensing image classification. They include supervised and
unsupervised approaches. Accuracy assessment is a valuable tool and a critical step for determining the
quality of the information derived from remotely sensed data. In this research were used supervised
and unsupervised techniques on remote sensing data, for land cover classification and to evaluate the
accuracy result of both classification techniques. SPOT5 satellite image was used for this research. The
land cover classes for the study area were classified into eleven classes of land cover. The accuracy
assessment of classification was achieved by training sample two hundred (200) samples points were
collected by GPS using Systematic Random Sampling. The results showed that the overall accuracy
for the supervised classification was 87%; where Kappa statistics was 85%. While the unsupervised
classification result was 84% accurate with 81% Kappa statistics. In conclusion, this study found that
the supervised classification technique appears more accurate than the unsupervised classification.
Keywords: Remote sensing, land cover mapping, accuracy assessment, SPOT 5 satellite image,
unsupervised classification, supervised classification.
List of symbol
GIS Geographic Information System
LCCS Land Cover Classification System
UTM Universal Transfers Merector
GPS Global Positioning System
B.A Bare Areas
2 IMZAHIM ABDULKAREEM, AKRAM KHLAIF
T.W Urban, Rural and Industrial Areas
S.V.A Sparsely Vegetation Areas
N.V Aquatic or Regularly Flooded Natural Vegetation
M.L Marshlands
W.B Water Bodies
W.C Water Courses
U.R Temporary Waterbodies / Waterlogged Areas
S.D Sandy Area and Dunes
I.C Irrigated Cropland
M.A Marginal Agriculture
ERDAS Earth resources data analysis system
SPOT Satellite Pour Observation Terre
LCCS Land Cover Classification System
1. INTRODUCTION
The production of thematic land-cover maps is one of the most common applications of
remote sensing. These land-cover maps support a large range of research efforts studying
characteristics of the earth’s surface, especially land use planning and environmental studies
[1].
Classification in remote sensing involves clustering the pixels of an image to a set of classes,
such that pixels in the same class are having similar properties. The majority of image
classification is based on the detection of the spectral response patterns of land cover classes.
Classification depends on distinctive signatures for the land cover classes in the band set
being used, and the ability to reliably distinguish these signatures from other spectral
response patterns that may be present. There are many different approaches to classifying
remotely sensed data. However, in common they all fall under two main topics: unsupervised
and supervised classification technique [2].
In remote sensing-land cover mapping study, accuracy assessment is important to evaluate
remote sensing final product. The purpose of assessment is important to gain a warranty of
classification quality and user confidence on the product. Normally, accuracy result are
derived from supervised or unsupervised or both techniques. However supervised and
SUPERVISED AND UNSUPERVISED ALGORITHM FOR LAND COVER………. 3
unsupervised technique relatively shows different level of accuracy after accuracy assessment
was performed [3].
The accuracy of an image classification depends on the number of samples taken for each
class and the quality of reference images used for analysis. The accuracy assessment usually
evaluates the effectiveness of classifiers with the help of field data by testing the statistical
significance of a difference through computation of kappa coefficients and the overall
accuracies [4].
2. STUDY AREA
The area of study is Thi-Qar governorate which extends from Waist governorate in the north,
to the Basra governorate in the south, to Mesian governorate, east to Al-Muthana governorate
, west in Iraq area and zone (38 N) according to UTM geographic coordinate system .Its
length is about 161.5 km with width between (55-142) km. Local study region extends
between latitude (30°40' 00" to 32° 00' 00") north and longitude (45°40' 00" to 47°10' 00")
east, with an area of about (13751.6) km2, It was measured using ArcGIS 9.3 as shown in
Figure (1).
3. THE DATA AVAILALE
In this research the data used can be classified in the following two types:
3.1 Earth observation data
SPOT5 image one of satellite images were acquired, to perform the classification itself and to
support the process.
SOOT5 image: Taken on 13-May-2011. SPOT5 is equipped with three monitoring systems,
the HRG- (High Resolution Geometric), HRV- (High Resolution Visible) and HRS-
instrument (High Resolution Stereo). The HRG instrument uses the panchromatic mode with
a resolution of 2.5 respectively 5 meters [5].
3.2 Field surveying data
Field survey focus on field data collected. The aim of field survey is to collect land cover
information through field samples. This phase helps in validating maps derived from satellite
imagery analysis. Field data describe the truth on the ground and are collected to confirm or
correct the preliminary photo-interpretation.
4 IMZAHIM ABDULKAREEM, AKRAM KHLAIF
The field surveying data form has been used to record the land cover classification types
which are used later in land cover classification system (LCCS).
4. METHODOLOGY
4.1 Layer Selection and Stacking
The Layer stacking combine multiple derivate image measures (texture, independent
components, and so forth) into a single multi-band image to improve classification accuracy.
Layer Re-Stack for SPOT5
Four bands of SPOT5 image were prior stacked in to a false color order (i.e., near infrared,
red, green, SWIR) due to absence of blue band. Layer stack bands in to (green, red, near
infrared, SWIR) order are carried out by using ERDAS Imagine 2013spatial modeler.
Generally, the necessity of stacking layers is to produce a combined image to facilitate the
analysis and processing stages.
4.2 HPF Resolution Merge
Use the HPF resolution merge function to combine high-resolution panchromatic data with
lower resolution multispectral data, resulting in an output with both excellent detail and a
realistic representation of original multispectral scene colors. SPOT5 makes it possible to
produce high resolution multispectral images by merging panchromatic and multispectral
data. Principal component Pan sharpen of SPOT5 image is performed with ERDAS Imagine
2013 to produce a 2.5m resolution color image from 10m resolution of multispectral bands
plus a corresponding high resolution panchromatic band multispectral data.
4.3 Image Mosaic
Image mosaic is to link the process or assemble a set of overlapping satellite scenes, for the
production of a comprehensive and integrated large scene any space large image. That these
images are homogeneous in terms of accuracy discrimination spatial, lighting, contrast in
addition to possessing the qualities of the other image map and the scale and projected
Suitable. Image mosaic have great importance due to "frequent use in the production of maps
pictorial and invocation and give the general perception of the overall area covered by the
image that.
The study area covers on eight satellite scenes of the satellite SPOT5m Figure No (2). Which
required us to configure one scene covers the study area, then Image Subset area of interest
SUPERVISED AND UNSUPERVISED ALGORITHM FOR LAND COVER………. 5
(the limits of Thi-Qar province), so it was recorded contiguous visualizations engineered,
every one of the other highlight points ground control and overlay areas (Overlap) and delete
cells (Pixels) multiplexed digital seed (s) to overlapping areas. The Subset area of interest
was done in satellite images Arc Map 9.1 Figure No. (3) Showed the subset area of interest
from SPOT5.
5. DEFINITION OF THE LEGEND FOR THE LAND COVER MAP
The first step for the Land Cover classification of satellite images is the definition of the
legend to be used for land cover system. The legend adopted for this work is based on the
unique international land cover system up to now: the Land Cover Classification System
(LCCS). In this work an own classification system was defined. There are eleven information
classes need to be identified by automatic image classification as shown in Table No (1).
6. CLASSIFICATION
There are two primary types of classification algorithms applied to remotely sensed data:
unsupervised and supervised
6.1 Unsupervised Classification with SPOT 5m Image
In this case, a SPOT 5 image with 4 bands from three spectral regions (Green, Red and NIR)
was used for unsupervised classification. Performing unsupervised classification, some
parameters such as number of classes, maximum iterations, convergence threshold, need to
be specified. In the case of performing unsupervised classification on the SPOT5 image, the
number of classes specified is eleven, which means by unsupervised classification, eleven
spectral classes need to be identified: the maximum number of iterations is 10 and the
convergence threshold is 0.950, as shown in Figure No (4).
6.2 Supervised Classification with SPOT5 Image
Supervised classification of SPOT5 image with bands selected using training samples
collected directly from the fieldwork Figure No (5). The training samples comprise of classes
belonging to (Sparsely vegetation area, Bare areas, Sandy area and dunes, Water courses,
Irrigated cropland, Marginal Agriculture, Aquatic or regularly flooded natural vegetation and
Temporary water bodies/waterlogged areas). Supervised classification shown in Figure No
6 IMZAHIM ABDULKAREEM, AKRAM KHLAIF
(6). The number of pixels in a training area for a given class was decided based on the rule of
thumb 50 sample for each class.
7. THE RESULT
The results of image analysis include results of unsupervised classification and supervised
Classification of the SPOT5 image.
7.1 Unsupervised Classification Result with SPOT5 Image
The result of unsupervised classification is only a number of spectral clusters. In the case of
unsupervised classification of the SPOT5 image, there are eleven spectral clusters identified.
Combining the spectral clusters, seven land cover types were identified from the
unsupervised classification result. They are sparsely vegetation area, bare areas, sandy area
and dunes, water courses, irrigated cropland, marginal agriculture and aquatic or regularly
flooded natural vegetation. There are three land cover types that cannot be identified properly
by grouping the spectral classes. They are Water courses, Water bodies in additional
Marshland. The unsupervised classification result with seven land cover types is presented in
Figure No (7).
Accuracy assessment by error matrix
Accuracy assessment for unsupervised classification of SPOT5 can be evaluated from the
error matrix, it can be seen that the classification has an overall accuracy of 84% and Kappa
81%. The total area of each class showed in Table No (2) and histogram in Figure No (8) for
SPOT5 image.
7.2 Supervised Classification Result with SPOT5 Image
Supervised classification result of SPOT5 is given in Figure No (9). There are eleven land
cover classes identified.
Accuracy assessment by error matrix
Accuracy assessment of the supervised classification of SPOT5 image by error matrix
includes the error matrix by random sampling.
The overall accuracy with random sampling is 87% and Kappa 85%. The random points
(pixels) can be located at the boundary of classes and those pixels may have mixed spectral
reflectance information from different classes and are therefore not always classified
correctly.
SUPERVISED AND UNSUPERVISED ALGORITHM FOR LAND COVER………. 7
Histogram for this classified image is given in Figure No (10) and the total areas for each
class in Table No (3).
CONLUTIONS:
1. The Land Cover Classification System (LCCS) of FAO was followed as guide in the
classification processes. This approach defined and determined the land cover classes to be
included in the classifications. These classes were defined before starting each classification.
2. The classification of the remotely sensed data was based on the traditional pixel-based
classification method. The results of classifications were always presented as thematic maps.
The results of the various tested approaches and algorithms of classification on the various
obtained remote sensing data were interpreted based on the accuracy assessment method.
3. This research has provided the complete information about the land cover for Thi-Qar
governorate. The overall analysis accuracy was 87% for supervised classification and 84%
for unsupervised classification.
4. The research find stressed the importance of supervised and unsupervised classification
for SPOT5 satellite image in the survey and classification of land cover large areas, as she
was quick and effective tool to get the results in less time and cheaper costs and reduction
effort.
Figure 1: Study Area Location.
8 IMZAHIM ABDULKAREEM, AKRAM KHLAIF
Figure No (2) SPOT5 mosaic processing of study area
Figure No (3) Image Subset area of interest SPOT5 (Thi-Qar province)
Figure No (4) Unsupervised classification windows for SPOT5 image
SUPERVISED AND UNSUPERVISED ALGORITHM FOR LAND COVER………. 9
Figure No (5) Training sample for classification
Figure No (6) Supervised classification windows
Study area
image
Output
classified
image
Signature
Editor
Type of
classification
10 IMZAHIM ABDULKAREEM, AKRAM KHLAIF
Figure No (7) Unsupervised SPOT5 classification
result
SUPERVISED AND UNSUPERVISED ALGORITHM FOR LAND COVER………. 11
1273.915
747.22
280.88505.278
397.797
1005.632
903.307
Bare area
SPARSELY VEGETATED AREAS
WATER COURSES
AQUATIC OR REGULARLY
FLOODED NATURALVEGETATIONIRRIGATED CROPLAND
SANDY AREAS AND DUNES
MARGINAL AGRICULTURE
Figure No (8) Area for each class in the total area for unsupervised SPOT5 image
12 IMZAHIM ABDULKAREEM, AKRAM KHLAIF
Figure No (9) Supervised classification result of SPOT 5
285.36
184.14
243.04
2621.92
1081.33
56.22
1857.23
3877.45
363.91
614.541198.28
TEMPORARY WATERBODIES /WATERLOGGED AREASWATER BODIES
MARSHLANDS
BARE AREAS
SANDY AREAS AND DUNES
WATER COURSES
SPARSELY VEGETATED AREAS
MARGINAL AGRICULTURE
URBAN, RURAL AND INDUSTRIALAREASIRRIGATED CROPLAND
AQUATIC OR REGULARLYFLOODED NATURAL VEGETATION
Figure No (10) Area for each class in the total area for supervised SPOT5 image
Total Area in KM2
SUPERVISED AND UNSUPERVISED ALGORITHM FOR LAND COVER………. 13
Table No (1) Land cover classes with photo for each class
No. Class name Class photo
1 TEMPORARY WATERBODIES /
WATERLOGGED AREAS
2 WATER BODIES
3 MARSHLANDS
4 BARE AREAS
5 SANDY AREAS AND DUNES
6 WATER COURSES
7 SPARSELY VEGETATED AREAS
8 MARGINAL AGRICULTURE
9 URBAN, RURAL AND INDUSTRIAL AREAS
10 IRRIGATED CROPLAND
11 AQUATIC OR REGULARLY FLOODED
NATURAL VEGETATION
14 IMZAHIM ABDULKAREEM, AKRAM KHLAIF
Table No (2) Total area for each class for unsupervised SPOT5 image
Table No (3) Total area for each class for supervised SPOT5 image
No. Class name Total area Km2
1 Unclassified 10131.56
2 Bare area 3299.42
3 SPARSELY VEGETATED AREAS 1935.29
4 WATER COURSES 727.47
5 AQUATIC OR REGULARLY FLOODED NATURAL
VEGETATION 1308.66
6 IRRIGATED CROPLAND 1030.28
7 SANDY AREAS AND DUNES 2604.57
8 MARGINAL AGRICULTURE 2339.55
No. Class name Total area Km2
1 Unclassified 10131.56
2 TEMPORARY WATERBODIES / WATERLOGGED
AREAS 285.36
3 WATER BODIES 184.14
4 MARSHLANDS 243.04
5 BARE AREAS 2621.92
6 SANDY AREAS AND DUNES 1081.33
7 WATER COURSES 56.22
8 SPARSELY VEGETATED AREAS 1857.23
9 MARGINAL AGRICULTURE 3877.45
10 URBAN, RURAL AND INDUSTRIAL AREAS 363.91
11 IRRIGATED CROPLAND 614.54
12 AQUATIC OR REGULARLY FLOODED NATURAL
VEGETATION 1198.28
SUPERVISED AND UNSUPERVISED ALGORITHM FOR LAND COVER………. 15
ACKNOWLEDGMENT
The support of this research by the Iraq- Ministry of higher education and scientific research
– University of Technology – Building & Construction Engineering Department.
REFERENCES
[1] Maria I. M., "Mapping Spatial Thematic Accuracy Using Indicator Kriging", M.Sc.
Thesis, University of Tennessee, 2013.
[2] Mohd H. I., Pakhriazad H. Z. and Shahrin M. F., “Evaluating Supervised and
Unsupervised Techniques for Land Cover Mapping Using Remote Sensing Data”, Geografia
OnlineTM Malaysian, Journal of Society, Space 5, issue 1, ISSN 2180-2491, 2009, pp 1 – 10.
[3] Mario L. C., “Developing A Land Cover Classification System for The Upper Paraguay
River Basin Using Remotely Sensed Imagery”, MSc. Thesis, University of Memphis, 2003.
[4] Jambally M., "Land use and cover change assessment using Remote Sensing and GIS",
International Journal of Geomatics and Geosciences, University of Duhok, Iraq, Vol. 3, No
3, 2013, pp 552-569.
[5] CNES (2002): Instrument features. Spectral bands - Resolution – Swath
http://spot5.cnes.fr/gb/satellite/satellite.htm , 07. 2002.