XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/4: Water]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS Nguyen Dinh Duong

Document type:: Multivolume work

Structure type:: Chapter

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

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