International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
     
WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL 
PATTERN ANALYSIS 
Nguyen Dinh Duong 
Department of Environmental Information Study and Analysis, Institute of Geography, 
18 Hoang Quoc Viet Rd., Cau Giay, Hanoi, Vietnam — duong.nguyen2007@gmail.com 
Commission VIII, WG VIIUA 
KEY WORDS: Remote sensing, Hydrology, Classification, Automation, Spectral pattern 
ABSTRACT: 
Water is one of the vital components of the Earth environment which needs to be frequently monitored. Satellite multispectral remote 
sensing image has been used over decades for water body extraction. Methodology of water body extraction can be summarized to 
three groups: feature extraction, supervised and unsupervised classification and data fusion. These methods, however, are of pure 
mathematical and statistical approach and little of them explore essential characteristics of multispectral image which is based on 
ground object radiance absorption behaviour in each sensing spectral bands. The spectral absorption characteristics of water body in 
visible and infrared bands differ very much from the other ground objects. They depend only on the used spectral bands and can be 
considered as invariant and sensor independent. In this paper the author proposed an application of spectral pattern analysis for water 
body extraction using spectral bands green, red, near infrared NIR and short wave infrared SWIR. The proposed algorithm has been 
used for water body extraction by Spot 5 and Landsat 5 TM images. Ground truth validation was carried out in Hanoi City. The 
advantage of this algorithm does not base on water body extraction only but it allows to asses also water quality. Different level of 
turbidity and organic matter contents could be classified by using additional index. 
1. INTRODUCTION 
Due to the importance for the Earth environment water has been 
monitored for a long time. Recently remote sensing approach in 
water recognition has been intensively studied and satellite 
remote sensing image data has been recognized as very useful 
information source for water extraction. Methodologies of water 
body extraction can be summarized to three groups: feature 
extraction, supervised and unsupervised classification and data 
fusion. Rajiv Kumar Nath et al. (2010) provided comprehensive 
overview on methods on water extraction from high resolution 
satellite images. Fu June et al. (2007) developed an automatic 
extraction of water body from TM image using decision tree 
algorithm. The proposed algorithm is based on spectral 
characteristics of water body in TM images. From the 6 visible 
bands author selected four bands b,, bs, bs and bs for 
development of decision tree algorithm which allows 
automatically water extraction. The algorithm is based on 
reflectance threshold for band by, relation between by, bs and b, 
and bs and relation between sum of b;, by and by, bs. Validation 
was done by visual checking of analysis and manual 
interpretation. Hua Wang and others (2008) developed water 
extraction method based on texture analysis. The algorithm 
works for high resolution panchromatic imagery. Spectral 
characteristics of water were not used in the proposed 
algorithm. Jiancheng Luo et al. (2010) presented very 
Interesting approach in water extraction using Landsat TM 
images. The algorithm combines water index computation, 
whole-scale segmentation, whole-scale classification and local- 
scale segmentation and classification to achieve high-precise 
water extraction result. More complicated algorithm has been 
proposed by Min Li et al (2011) for water body extraction based 
on oscillatory network. Panu Nuangjumnong (2009) used Spot 
pan-sharpened image for water extraction. There are many other 
methods for water extraction which can be named in long list. In 
this paper the author proposed quite original algorithm for water 
extraction. Nguyen Dinh Duong (1997) proposed a method for 
decomposition of multi-spectral image into several sub-images 
based on modulation (spectral pattern) of the spectral 
reflectance curve. The hypothesis roots from the fact that 
different ground objects have different spectral reflectance and 
absorption characteristics which are stable for a given sensor. 
This spectral pattern can be considered as invariant and be used 
as one of classification rules. Water body is commonly 
understood as collection of water on the Earth surface. They are 
ocean, lakes, rivers, ponds and others geographical features. It is 
very easy to recognize water body in conventional survey 
practice. Remote sensing, however, observes the Earth surface 
in entire field of view of sensor and records unselectively 
ground objects. Water recorded in remote sensing images can be 
not only lakes, rivers, ponds, reservoir but also rice field after 
irrigation and others features. In this paper the author does not 
differentiate the true water body from temporal water surface. 
2. METHODOLOGY 
2.1 Spectral Pattern of Water body 
Given a dataset with four spectral bands: green, red, near 
infrared and short wave infrared X(b;,by,b3,bs) where the b; 
stands for reflectance in band i converted from digital number 
DN of each pixel using the gain and bias coefficients provided 
along with image data. A spectral reflectance curve can be 
constructed by simple plotting of reflectance values in a graph 
as showed on Figure 1. If we use the C for value of relative 
positions of each vertex then modulation or spectral pattern of 
the spectral reflectance curve could be expressed as: 
C12C13C14C23C24Cs4- The C value is of (0, 1, 2}. For example