principal techniques of image classification in remote...

Principal Techniques of Image Classification in Remote Sensing

Emaad Ansari*, Mohammed Nishat Akhtar*, Mohamad Khairi Ishak**, Hiroaki Uchida***

Elmi Bin Abu Bakar*

*School of Aerospace Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal,

Penang, Malaysia

**School of Electrical and Electronics Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal,

Penang, Malaysia

***Department of Mechanical Engineering, National Institute of Technology, Kisarazu College,

Kisarazu, Japan

(Corresponding author e-mail: [email protected]).

Abstract: Image classification techniques are one of the most widely used methods of analyzing images

using image processing algorithms. The need of assessing remote sensing applications for good

comparison among the land also requires image classification. The main image classification techniques

usually incorporated in remote sensing are unsupervised image classification, supervised classification,

and object-based image analysis. The current study incorporates the procedure of these image

classification techniques. The methodology of each technique using flow chart and examples are

explained and along with it its use in research of remote sensing data is briefly summarized in the present

work also. Overall, a quick review of image classification techniques is taken into the study.

Keywords: Image classification, algorithm, remote sensing, unsupervised image classification, supervised

classification, object-based image analysis.

1. INTRODUCTION

The process which involves land cover classes such as water,

urban cities, agricultural land, grassland, forest, etc. are

assigned to pixels is known as Image classification. Although

image classification techniques are widely available; with

respect to remote sensing, widely used principal techniques

involve unsupervised image classification, supervised

classification, and object-based image analysis. Unsupervised

image classification and supervised image classification are

most simple, quick and extensively used technique among

them. But, the need of high resolution data paved out the way

for object-based classification nowadays. The image

classification techniques and algorithms have been previously

used in number of research. Some of them appeared in the

literature.

Lake Mutrikwi, Zimbabwe was analysed using LANDSAT

images from the year 1984 to 2011 (Dube et al., 2014). Land

cover changes were observed using the maximum likelihood

algorithm which is considered as the best image classification

algorithm by researchers. Normalised difference vegetation

index was calculated to evaluate the aquatic weeds floating

on the surface (Dube et al., 2014). Spectral-Spatial

classification techniques was surveyed using attribute profiles

(Ghamidi et al. 2014). The modification and standardisation

in the attribute profiles is extensively studied and focus is

given on its use in remote sensing image classification.

Emphasis is also given on monitoring crops, urbanisation,

and risk management using attribute profiles (Ghamidi et al.

2014). Approaches and techniques of image classification

play a vital role in computer imagination (Kamavisdar et al.,

2013). Image classification was referred as labelling the

images into various categories. Artificial neural networks was

also used for image classification. The classification was

conducted on the basis of sensors, pre-processing, detection,

segmentation, and feature identification (Kamavisdar et al.,

2013).

Image classification techniques were segregated on the basis

of pixel, sub-pixel and objects (Li et al., 2014). The

importance of spatial information in the area of remote

sensing is given core importance. Two case studies were

conducted to group spatial information on the basis of

texture, Markov random fields (MRF) and image

segmentation. The advancement in object based analysis

using image segmentation is highlighted in the review (Li et

al., 2014). Crop coverage identification was experimentally

evaluated using image classification techniques applied on

the remote sensing images (Omkar et al., 2008). Image

classification was proved as an only solution for problems

occurring in the study of remotely sensed images. Maximum

likelihood classifier, Ant colony optimisation and particle

swarm optimisation algorithms were used in the comparative

study (Omkar et al., 2008). Land use and land cover (LULC)

map analysis was conducted using image classification

techniques such as Artificial Neural Network (ANN),

Classification Trees (CT) and Support Vector Machines

(SVM) (Salah, 2017). The selection of sample size,

parameters setting for classification, and combination of

classifiers are explained in the study. A proper selection of

MACE Technical Journal (MTJ) MTJ Vol.2(01) [December 2020], pp. 21-25 eISSN: 2710-6632

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image classification techniques is a pivot for good accuracy

(Salah, 2017).

Remotely sensed images captured using different sensors

analysed through different algorithm provides fluctuating

accuracy on a changing time period (Sisodia et al., 2014). Mahalanobis, Maximum Likelihood Classification, Minimum

distance and Parallelepiped classification were used on

images of the year 1972, 1998, and 2013. Fluctuating

accuracies were achieved using same algorithms on different

time. Accuracy was defined using producer, user, overall

accuracy as well as kappa stats (Sisodia et al., 2014). Image

classification algorithms such as ANN, fuzzy sets and genetic

algorithms were used with remotely sensed data for thorough

comparison (Stathakis and Vasilakoss, 2008). SVM iamge

classification technique was used to determine LULC in the

tropical coasts (Szuster et al., 2011). Hyper-spectral images

were classified using MRF and SVM techniques and

accuracy was evaluated using both techniques (Tarabalka et

al., 2010). Thus, on numerous occasions, the need and

application of image classification techniques is reflected in

the literature. The current study provides simple and effective

steps in the image classification techniques using various

available algorithms suitable for remote sensing applications.

2. UNSUPERVISED CLASSIFICATION

Fig.1. Steps involved in unsupervised image classification

Fig. 2. Remotely sensed image classified using unsupervised

classification (El-Din et al., 2013)

Unsupervised classification consists of a simple two stage

procedure in which initially the clusters are generated using

pixels and then different morphological features of the land

are segregated depending on the clusters, refer to Fig. 1. As

this technique does not requires large sample of data, it forms

an easy and quick technique. Although a time saving

approach, still unsupervised classification is having the

lowest accuracy among the three principal techniques

considered in the study. The technique is usually equipped

with basic algorithms like K-Means or ISODATA. The

clustering algorithms helps in creating groups which in turn

can be given classes manually and the image can be analysed.

Fig. 2 shows an example of unsupervised classification

wherein 10 classes or groups of land are generated using the

algorithm (El-Din et al., 2013).

3. SUPERVISED CLASSIFICATION

The only difference between the supervised and unsupervised

image classification technique is creating the sample. Here in

supervised classification, each land cover is represented by a

sample. This in return helps in developing a signature file.

This signature file is then applied on the entire set of images

and thus helps in getting the various classes automatically in

the entire image and the image is classified, refer to Fig. 3.

The widely used algorithms for supervised classification

involves Minimum distance, Maximum Likelihood, SVM,

Iso-Cluster and Principal Component’s algorithm.

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Fig. 3. Steps involved in supervised image classification

A sample image classified using supervised classification is

shown in Fig. 4. Various land class such as seawater,

sediments, marsh, urban, grass, fields, etc. are manually

being created in the image (Lopez, 2016). These classes will

be considered as a signature file and using a suitable

algorithm the entire image can be classified. The signature

file helps in classifying the entire image depending on the

pixels of sample training sites. SVM is the most widely used

algorithm in supervised image classification.

Fig. 4 Training sites created as sample in Supervised

Classification (Lopez, 2016)

4. OBJECT BASED IMAGE ANALYSIS (OBIA)

Fig. 5. Steps involved in object-based image classification

In the previous two techniques, the classification creates

pixels and each pixel is given class either manually as in

unsupervised classification or by signature file as in

supervised classification. On the contrary, object based

analysis groups’ pixels into representative shapes with a size

and geometry. The image is segmented into different objects

and training areas are created using the sample objects. In the

training areas, features of the objects are added and finally

images are classified (Fig. 5).


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Fig. 6. Segmentation of image highlighting buildings (Image

Courtesy: gisgeography.com)

Fig. 6 shows a segmentation of image highlighting the

buildings which is a step of object based image classification.

Segment mean shift in ArcGIS and multi-resolution

segmentation in ECognition are the widely used algorithms

for image segmentation. Another advantage of OBIA is that

objects can be classified either by using shape, texture,

spectral properties or geographical context. Object based

image analysis can achieve maximum accuracy among other

image classification techniques (Wieh and Riggan, 2010). A

high resolution image also has a greater impact on increasing

overall efficiency (Wieh and Riggan, 2010). On a coal fire

land, object based classification achieves an accuracy of

36.77 % higher than that of pixel based classification (Yan et

al., 2006).

5. TRENDS IN IMAGE CLASSIFICATION TECHNIQUES

USED IN REMOTE SENSING

OBIA is making an extra-ordinary progress in image

classification and large amount of research publications are

published using OBIA ((Blaschke, 2010). The trend of the

principal techniques of image classification is shown in Fig.

7. Although image classification using pixels is an easy and

time saving technique, object based classification is gaining

momentum due to its high accuracy. Still, in practice, the

supervised classification is most widely used due to

satisfactory accuracy and ease of classification. But the

unsupervised classification is now on down trend since it

achieves very low accuracy and thus disturbing the remote

sensing data. In recent studies, use of semi-supervised image

classification is also being reflected (Wu et al., 2020).

Attention mechanism and generative adversarial networks are

used for semi-supervised image classification (Xiang et al.,

2020). Semi-supervised image classification is also finding

applications in medical imaging (Liu et al., 2020).

Fig. 7. Trend of image classification technique in research

publications (Image Courtesy: gisgeography.com)

6. DISCUSSION AND CONCLUSION

A brief description of image classification techniques such as

pixel based techniques and object based techniques is pointed

out in the study. Ease in using unsupervised and supervised

image classification techniques is discussed. The trends in

recent times stating out the downtrend in unsupervised

classification and uptrend visible in supervised classification

is also noted. Accuracy of multi-spectral images can still be

increased to some extent when used along object based image

analysis algorithms. Further study of semi-supervised image

classification can be considered as an extended work.

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