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.