2012 Second International Workshop on Earth Observation and Remote Sensing Applications

978-1-4673-1946-1/12/$31.00 ©2012 IEEE.

Comparison of Pixel-Based and Object-Based Classification for Glacier Change Detection

Ibad-Ur-Rehman Raza [1], Syed Saqib Ali Kazmi [2], Syed Saad Ali [3], Ejaz Hussain [4]

Institute of Geographic Information Systems, School of Civil & Environmental Engineering, National University of Sciences &

Technology

Islamabad, Pakistan

Ibad11@igis.nust.edu.pk [1], saqib11@igis.nust.edu.pk [2], saad11@igis.nust.edu.pk [3], ejaz@igis.nust.edu.pk [4]

Abstract— This research paper compares the result of Object

based and Pixel based classification techniques for glacier

change detection on Landsat Thematic mapper (TM) and

Enhanced Thematic Mapper (ETM+) imageries. The objective

of this study is to see which classification method performs better

for change detection in mountainous regions. Northern face of

Himalayan region, The study area is undergoing climate change

in the form of rapid melting of glacial ice mass, expansion of the

existing lakes and creation of new lakes. This results in Glacial

Lake Outburst Flood (GLOF) and breach or outburst from ice

and ‘moraine dams’ causing devastating floods downstream. The

global warming phenomenon worldwide has resulted in a

significant decrease in glacial cover. Glacier’s change monitoring

and permafrost-related hazards have long been studied using

remote sensing data and techniques to assess the damage. The

world is facing a serious problem of handling the climate change

issue and its effects on humans as well as on natural resources.

Glaciers are considered as one of the best indicators of climate

change [1]. Landsat TM/ETM+ images were used for glacier

change monitoring of Turkey’s mountains project, Mount

Suphan. The results show that about ¾ of total area of suphan

glacier has been lost in 23 years. Traditional image classification

methods use only the spectral information at pixel level without

considering the shape of underlying objects [2]. However, object-

based image classification process uses spectral and spatial

dimensions (shape of feature) in order to perform classification.

In this study, multi temporal Landsat TM and ETM+ image from

1990 to 2010 have been used. Initially, the traditional pixel-based

classification was performed on Landsat thematic layers and

layers developed from indices like NDVI and NDSII. Then

object-based classification of these images was carried out. The

comparison of the classification results (both qualitative and

quantitative) show that the object-based approach gives about

10-15% higher accuracy, much better results in terms of area

estimation and change detection of snow covered areas as

compared to traditional pixel-based classification. The results

also indicate that object based classification is more useful in

mountainous regions to avoid confusion among classes produced

by shadows.

Keywords-Object Based, Change Detection, Glaciers,

Classification, Remote Sensing

I. INTRODUCTION

A Glacier is a huge accumulation of ice and snow which is

formed over a period of centuries when it exceeds its ablation

(melting and sublimation). A glacial body is usually more than

0.1 km² in area and its thickness is over 50 m. However due to

high pressure of overlaying ice and debris and constantly

increasing mass the glaciers slowly deform and flow or moves

downhill under its own weight. As a result this glacial flow, a

huge volume of rocks and debris is transported downhill and

develops landforms like cirques and moraines. Glaciers are

much different from the polar ice which is developed in the

polar regions of the earth and remains almost stagnant and

much less denser. Glaciers on the other hand form on elevated

land and continue to move.

The man induced climate change can be accessed from the

mountain glaciers and how they are rapidly changing through

time because they are the key to early warning system [3]. The

recent climate change and its effects on the human

communities and natural resources is the biggest challenge the

world faces. The biggest natural hazard that climate change

has produced in the recent years is the sudden melting of

mountainous glaciers resulting in catastrophe [4].The

Himalayan Range is facing the effects of climate change.

There are a number of Glacial Lake Outburst Flood

events that have occurred all around the world, especially in

Asia [5]. One of the devastating GLOF was in Gojal Tehsil

Hunza, Pakistan in 2010. The trend in global warming

induced by climatic changes indicate rapid glacial

retreat/melting leading to formation of glacial lakes on

mountains and creating a potential for sudden devastating

floods due to breach or outburst from ice or moraine

„dams‟. In the recent past the glacial cover depleted

significantly all around as the climate becomes warmer.

Remote sensing is the only reliable source of data for spatio-

temporal analysis to detect the glacial changes due to natural

phenomena or human induced global warming [6]. Remotely

sensed multispectral images taken at different frequency can

help to effectively monitor the recession and changes in the

glaciers and to determine the spread/extent of melted ice in the

adjacent areas. This study focuses on the Pixel-based and Object-based

classification method for glacial change detection. For this purpose, multispectral images of the northern face of Himalayan Range over a period of three decade have been chosen to indicate the changes in the glacier‟s tongue due to climate change. Furthermore these two classification methods

2012 Second International Workshop on Earth Observation and Remote Sensing Applications

978-1-4673-1946-1/12/$31.00 ©2012 IEEE.

have been used to compare not only the glacial change but also their performance.

II. STUDY AREA

The study area is the National park region which is at the northern side of Mount Everest, highest mountain peak.

The Himalayan mountain range lies immediately north of the Indian sub-continent. Himalayas range from the Indus river valley in west to the Brahmaputra river valley in east, forming an arc 2,400 km. It shares the border of Nepal and China. The study area is shown in “Fig 1”.

III. METHODOLOGY

Landsat TM data of National park region is acquired for a period of 30 years (1990, 2001, and 2010). The images are atmospherically corrected and then classified using both the pixel-based and object-based techniques.

A. Pixel based Method

Firstly, the pixel based technique using Maximum likelihood classification is applied on all the three images. Images were classified for number of classes‟ Maximum Likelihood algorithm for pixel based classification is a classical classifier [7, 8]. ERDAS 2011 was used to perform Pixel Based supervised maximum likelihood classification. The traditional region growing method of classification was not effective on shadowed regions of the study area. This technique wrongly classified the shadow in soil class whereas the spectral pattern suggested that it was snow covered shadowed region. To overcome this confusion we used indices like NDVI and NDSII and then used region growing method.

Pixel based method is lengthy because a number of pure classes has to be taken which should be well distributed in the study area.

1) Indice Based To improve the pixel based technique NDVI and NDSII were applied to the images. The resulting thematic layers were stacked and then once again classified using pixel-based technique. Although there is no vegetation cover in all of the three images, still NDVI was chosen because it extracts soil from the image as well. This method was useful as it reduced the effect of shadows and normalized the images.

B. Object Based

Object based classification technique takes into account the shapes of the features along with their spectral pattern. In his method, the image is initially divided into small segments and then these segments are classified [9, 10]. There are three segmentation techniques namely: thresholding/clustering, region based, and edge based.

For object segmentation, 30% weightage was assigned to shape and 70% to spectral pattern. Shape is given relatively less weightage but it greatly increases the accuracy of segmentation. Then NDVI and NDSII are applied to extract the snow and soil cover from the three images. The resulting classified images were then converted to vector format for area quantification. The complete tree of the methodology followed is shown in Fig. 2.

Figure 2. The Methodology flow chart

Figure 1. Study Area Landsat Satellite Imagery

2012 Second International Workshop on Earth Observation and Remote Sensing Applications

978-1-4673-1946-1/12/$31.00 ©2012 IEEE.

IV. ACCURACY ASSESSMENT

To check the classification accuracy, random sampling technique is used. For this purpose, 173 random points were generated and then the reference image of study area are overlaid to access the accuracy. The results are given in “Table 1”.

Pixel

Based

Indices

Based

Object

Based

Kappa(K) 0.6416 0.7349 0.7889

Tau(τ) 0.6532 0.7314 0.7919

Overall

Accuracy

0.8266 0.8671 0.896

Table 1

V. RESULTS AND DISCUSSIONS

The results show that the Object-based approach towards glacier change detection is the more efficient approach and less time consuming. Moreover, this technique gives a more compact and qualitatively better look as it considers shape of the object in classification.

After computing the overall accuracy and Kappa coefficient for all the three methods of classification, we found out that the Object-based classification approach towards the quantification of glaciers is better than the pixel based methods.

However, indices based method used in pixel based classification helped to reduce the effect of shadow in the image and correctly classified snow and soil on sunlit side as well as on shadowed side. After accuracy assessment it was found out that soil was better classified through this technique.

Whereas, conventional pixel-based classification technique was less efficient in handling the shadows and mostly classified shadow as soil without having the knowledge of aspect and continuity of features. However, the pixel based techniques produced an abrupt appearance whereas the object-based approach produced smooth appearance. The classified images are shown in figures 3, 4 and 5 below.

From “Fig 3” it can be seen that shadow was not properly classified. The traditional Pixel-based classification technique has mixed the snow and soil classes. When indices were applied and the image was stacked together the shadow effect was removed and more accurate results were seen “Fig 4”.

After removing the effect of shadow, the last challenging thing was to compare these accurate results with the Object based approach in which the shape of the object is more important. The results were more or less similar but OB was more accurate “Fig 5”.

ACKNOWLEDGMENT

This work is performed in IGIS, National University of Sciences and Technology. We are thankful to Mirza Muhammad Waqar (IGIS, NUST) and Haseeb Ur Rehman (IGIS, NUST) for their help in this research. We are also thankful to the Lab staff for their support. We acknowledge the facilities provided by IGIS in the computer lab.

[[Figure

Figure 4. Indices Based

Figure 5. Object Based

2012 Second International Workshop on Earth Observation and Remote Sensing Applications

978-1-4673-1946-1/12/$31.00 ©2012 IEEE.

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