情報地質 第27巻 第2号 104-105頁 2016年
Geoinformatics, vol.27, no.2, pp.104-105, 2016

Pixel Based and Object Based Fuzzy LULC Classification using GRASS GISand RapidEye Imagery of Lao Cai Area, Vietnam
Thi Hang DO*, Venkatesh RAGHAVAN*, Poliyapram VINAYARAJ*, Xuan Luan TRUONG** andGo YONEZAWA**Graduate School for Creative Cities, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-8585, Japan. E-mail: dohanghumg@gmail.com.
**Hanoi University of Mining and Geology,
No.18, Vien street, Duc Thang ward, TuLiem district, Hanoi, Vietnam.
.

Key words：LULC, Classification, Fuzzy, Object-based, Pixel-based, GRASS GIS

1. Introduction
Remote sensing data is valuable source to extract
LULC information. RapidEye image – a high resolution
optical remotely sensed imagery with red-edge channel
which can improve LULC classification (Schuster et al.,2012). The fuzzy method which defines soft boundaries
dividing LULC objects has been applied successfully bymany authors (Zhang & Foody, 2010). In this study, fuzzy
LULC classification has approached at both pixel andobject level for Lao Cai area, Vietnam where there areboth urban, sub-urban and forest land cover exist.

2. Data and methodology
2.1. Data and Study area
The study area is located in Lao Cai province, themountainous area in the north of Vietnam. Main LULC
classes in the area are: built-up (urban, road, mining
activity area), water, baresoil, agriculture and vegetation(shrub, plantation, forest). The remotely sensed data
used is RapidEye multispectral Level 3A dataset
acquired on September 2014 covering the study area.
Reference data has collected from forest map inscale 1:10000 (Ministry of Agriculture & RuralDevelopment, Vietnam) in order to be used as training
data for classification and data for accuracy assessment.
2.2. Methodology
A small part of the RapidEye image covered by cloudhas been removed, so the study area is completely
cloud-free. Atmospheric correction and terrain correction
has been carried out as preprocessing to remove orreduce the influence of atmospheric and topographic
shadow effects. After pre-processing, fuzzy classification
is applied at pixel level and object level. Flow chart of themethodology has been shown in Fig. 1.
2.2.1. Pre-processing
The study area includes mountainous area so it isnecessary to remove noises affected by both atmosphericand topographic shadow effect. Atmospheric correction isdone by calculating Top of Atmosphere (ToA); topographic
correction has been effectively carried out by GRASS GIS
module “i.topo.corr”.

Fig. 1. Flow chart of fuzzy LULC classification
2.2.2. Fuzzy classification
As Zedeh mentioned: “Fuzzy logic aims at modelingthe imprecise modes of reasoning that play an essential
role in the remarkable human ability to make rational
decisions in an environment of uncertainty andimprecision”. The basic idea of fuzzy logic is that apropostion p is a collection of elastic constrains, C1, …,Ck, which restrict the values of variables X = (X1,…, Xn).
This is accomplished by representing p:
p → X is A
in which A is a fuzzy predicate. An n-array fuzzy relationin U: U = U1 x U2 x … x Un, and Ui, i = 1, …, n is thedomain of Xi. P implies that the possibility distribution ofX is equal to A:
πX = A
which in turn implies that:
Poss{X = u} = µA(u), u є U
Where µA is the membership function of A andPoss{X = u} is the possibility that X may take u as its

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value (Zedeh, 1988).
Fuzzy classification is Fuzzy Rule-based system inFuzzy logic in which input has to be transformed into
fuzzy statements (fuzzy sets). The input conjointly thefuzzy sets of rule base is caculated in the computational
unit, which return again fuzzy sets as output. The
process to transform from input to fuzzy sets called“fuzzification”, and the process transform from fuzzy sets
to output named “defuzzification”.
2.2.3. Pixel based fuzzy classification
Vegetation-Soil-Water Index (VSWI) includes Soilindex (S), Water index (W), Normalized Difference
Vegetation Index (NDVI), Normalized Difference Red
Edge Index (NDRE) and NDRE Soil index ratio(NDRE/S) have been used as rule based input data for
fuzzy analysis.
Full fuzzy logic standalone classification system“i.fuzzy.system” in GRASS GIS has been used to generate
the LULC map.
2.2.4. Object based fuzzy classification
Image objects are extracted from an image or groupof images to individualsegments based on gray tone,shape, etc. Segmented image has been created by using“i.segment” module in GRASS. Object basedIndex maps:
VSWI, NDVI, NDRE are generated from segmented
spectral bands.

3. Result and Discussions
Fig. 2 and Fig. 3 are showing the classified maps byFig. 2. LULC pixel based classification.

pixel based and object based fuzzy classification
respectively. Five classes: water, built-up, agriculture,
vegetation, baresoil have been demarcated by both pixel
based and object based classification. Accuracy
assessment has been carried out by comparing withreference sample andthe result of the accuracy
assessment has shown in Table 1. Even though, over all
accuracy of pixel based classification shows better resultwith accuracy of 72.25% compared with value 65.36% ofobject based, some classes (bare soil, agriculture) areshowing poor result. Especially, accuracy of agricultureclass is very low in both pixel based and object baseclassification, only 38.55% and 44.58% respectively,
because of similar spectral characteristic of the class andvegetation. Fluctuation in determining rules for fuzzyalso can be reason for low accuracy in those classes.

4. Conclusion and future work
In this paper, we examined performance of fuzzyclassification both in pixel based and object based
methods. Comparative evaluation shows that bothmethods are performing better some classes which othercannot present well. Therefore, the selective
combination/ fusion of both object based and pixel based
fuzzy classification could produce high accuracy results.
In the future work study will focus to improve the fuzzyclassification by combining object based pixel basedclassification by Dempster-Shafer Theory of Evidence(Shafer, 1976). Further also determination of rules for
fuzzy also can be improved collecting better field data.

Fig. 3. LULC object based classification.

Table 1. Accuracy of Pixel based and Object based LULC classification in percentage

Pixel based
Object based

Water (%)
82.04
74.17

Built-up (%)
74.66
59.18

Agriculture (%)
38.55
44.58

References
Jasiewicz J., (2010), A new GRASS GIS fuzzy inference
system for massive data analysis.Computers &Geosciences, vol.37, no.9, pp.1525-1531.
Schuster C., Forster M and Kleinschmit B., (2012),Testing the red edge channel for improving land-use
classifications based on high-resolution multi-spectral
satellite data. International Journal of RemoteSensing, vol.33, no.17, pp.5583-5599.

Vegetation (%)
87.09
73.20

Baresoil (%)
47.15
97.72

Total (%)
72.55
65.36

Shafer G., (1976), A Mathematical Theory of Evidence.
PrincetonUniversity Press. 314p.
Zadeh L. (1988), Fuzzy logic. IEEE Computer Society,vol.21, no.04, pp.83-93.
Zhang J., and Foody G.M., (2010), A fuzzy classificationof sub-urban land cover from remotely sensed imagery.
International Journal of Remote Sensing, vol.19,no.14, pp.2721-2738.