Satellite image classification using KL-transformation and ... ... Satellite image classification using KL-transformation and modified vector quantization Rafah Rasheed Ismail *,

Ismail and Al-Abudi Iraqi Journal of Science, 2016, Special Issue, Part B, pp: 428-440

_________________________________

Email:Moh 1972 [email protected] *

428

Satellite image classification using KL-transformation and modified vector

quantization

Rafah Rasheed Ismail * , Bushra Q. Al-Abudi

Department of Astronomy and Space, College of Science, University of Baghdad, Baghdad , Iraq.

Abstract

In this work, satellite images classification for Al Chabaish marshes and the area

surrounding district in (Dhi Qar) province for years 1990,2000 and 2015 using two

software programming (MATLAB 7.11 and ERDAS imagine 2014) is presented.

Proposed supervised classification method (Modified Vector Quantization) using

MATLAB software and supervised classification method (Maximum likelihood

Classifier) using ERDAS imagine have been used, in order to get most accurate

results and compare these methods. The changes that taken place in year 2000

comparing with 1990 and in year 2015 comparing with 2000 are calculated. The

results from classification indicated that water and vegetation are decreased, while

barren land, alluvial soil and shallow water are increased for year 2000 comparing

with 1990. Water, vegetation and barren land are increased, while alluvial soil and

shallow water decreased for years 2015 comparing with 2000. The classification

accuracy for the proposed method (MVQ) is 90.1%, 90.9% and 90.2% for years

1990, 2000 and 2015, respectively.

Keywords: Satellite images, classification, and Modified Vector

Quantization (MVQ).

( Modifide Vector Quntization)و (KLتصنيف صور االقمار الصناعية باستخدام تحويل )

بشرى قاسم العبودي , *رفاه رشيد اسماعيل , بغداد , العراق. قسم الفمك والفضاء , كمية العموم , جامعة بغداد

الخالصة لجبايش والمنطقة المحيطة بها في تم في هذا العمل تصنيف صور القمر الصناعي الندسات الهوار ا

ERDAS 2014 (MATLABباستخدام برنامجين ) 0102و0111و0991محافظة ذي قار لمسنوات Modified Vector. تم تطبيق طريقتين من طرق التصنيف المشرف عميها وهي الطريقة المقترحة ),7.11

Quantization )باستخدام برنامج MATLAB 7.11 وطريقة ( (Maximum likelihood Classifierبرنامج باستخدامERDAS imagine 2014 ذلك لمحصول عمى أدق النتائج ومن ثم مقارنة و

. وكذلك 0991بالمقارنة مع سنة 0111نتائج هذه الطريقتين و حساب التغييرات التي حدثت في سنة ج أن المناطق المائية ومناطق . أشارت النتائ0111بالمقارنة مع سنة 0102التغييرات التي حدثت في سنة

النباتات في حالة تناقص بينما مناطق المياة الضحمة والتربة الطينية واالراضي القاحمة في حالة زيادة في عام بينما أشارت النتائج أن المناطق المائية ومناطق النباتات واالرض القاحمة 0991بالمقارنة مع عام 0111

ISSN: 0067-2904

Ismail and Al-Abudi Iraqi Journal of Science, 2016, Special Issue, Part B , pp:428-440

429

بالمقارنة مع عام 0102المياة الضحمة والتربة الطينية في حالة تناقص في عام في حالة تزايد بينما مناطق

91.0% و91.9% و91.0( كانت MVQ. ان دقة التصنيف لطريقة التصنيف الموجهة المقترحة )0111 . عمى التوالي 0102و 0111و0991لمسنوات

1. Introduction

Remote sensing is the acquisition of physical data of an object, area or phenomenon without touch

or contact with the target under investigation using various sensors to detecting and recording

electromagnetic radiation from the target areas in the field of view of the sensors

instruments[1].Satellites remote sensing is equipped with sensors looking down to the earth. Since the

early 1960s, numerous satellite sensors have been launched into orbit to observe and monitor the Earth

and its environment. First Landsat satellite was launched in July 1972. Landsat satellites (series 1- 8)

providing repetitive, synoptic, global coverage of high resolution multispectral imagery, they have

potential applications for monitoring the conditions of the Earth's surface and the environment

components [2].

Digital image classification is the process of assigning pixels to classes. These classes form regions on

a map or an image, so that after classification the digital image is presented as a mosaic of uniform

parcels, each identified by a color or symbol. Image classification is an important part of the fields of

remote sensing, image analysis, and pattern recognition. The two general approaches which are used

most often are: supervised and unsupervised classification [1].

In this paper, we applied Korhunen- Loeve (KL) transformation on six bands of satellite image for

Al Chabaish marshes and the area surrounding district in (Dhi Qar) province for years 1990,2000 and

2015 using two software programming (MATLAB 7.11 and ERDAS imagine 2014) to create newly

integrated image with dense information and best contrast due to the information of all used bands are

concentrated in one integral image, two methods of classification have been used to classify area of

these newly images; these were supervised proposed method Modified Vector Quantization (MVQ)

using (MATLAB 7.11) and supervised methods (Maximum likelihood Classifier),using (ERDAS

imagine 2014) to get the most accurate results and then detect environmental changes in the study area

for the period 1990,2000 and 2015.This results are compatible with our field view which is done in (1

May 2016).

2. The Study Area

Al-Chibaish Marshes comprised a vast complex of mostly permanent freshwater marshes, with

scattered areas of open water, located to the west of the River Tigris and north of the River Euphrates

(within Basra, Thi-Qar, and Missan provinces). Covering an area of (3000 km2), it is bounded by the

longitudes 46° 47ʹ to 47° 21ʹ E and Latitude 30° 55ʹ to 31° 23ʹ N and represent many diverse habitats

from seasonal to permanent marshes. Figure-1, shows the original map of Iraq and the image inside

the polygon represents study area. While Figure-2 , show the study area for period 1990, 2000 and

2015 respectively. The satellite image for the study area is capture from landsat-7TM, lansat-7TM+

which are chosen bands are (1, 2,3,4,5 and 7) for years 1990 and 2000 and landsat-8OLI which are

chosen bands are (2,3,4,5,6 and 7) for year 2015, With 30 m spatial resolution.

Ismail and Al-Abudi Iraqi Journal of Science, 2016, Special Issue, Part B , pp:428-440

434

Figure 1- Location of the study area on the map of Iraq

Figure 2- The study area (Al-Chibaish Marshes and the area surrounding) for years 1990, 2000 and

2015

Ismail and Al-Abudi Iraqi Journal of Science, 2016, Special Issue, Part B , pp:428-440

434

3. Change Detection

Remote sensing techniques give quick methods to detect the environmental changes, such that

change detection. Change detection is a process of identifying differences in the state of objects or

phenomena by observing them at different time (multi-temporal analysis), therefore change detection

became useful tool for detecting land cover changes. The process of change detection is premised on

the ability to measure temporal effects. It has enabled to observe changes over large areas and

provided long-term monitoring capabilities. In general digital change detection techniques using

temporal remote sensing data are useful to help analyzed these data, and provided detailed information

for detecting change in land cover [3]. Image algebra is a widely used change detection technique that

involved one of two methods; band subtraction or the band ratio. The subtraction method involves

subtracting a digital number value from one date of a specified band from the digital number value of

the same pixel in the later date. The subtraction results in positive and negative values where change

has occurred. This method gives the information for compute the total area that has changed [4].

4. Image Transformation

Two-dimensional image transforms are very important in image processing. The image produce in

the transformed may be analyzed, explicate, and further processed for investigation diverse image

processing tasks. These transformations are exceedingly used, since by using these transformations, it

is conceivable to express an image as a combination of a set of basic signals, known as the basis

functions. Such transforms, reveal spectral structures embedded in the image that may be used to

characterize the image [5]. Image Transformatio