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Spectral mixture for remote sensingl...

Ponzoni, Flavio Jorge.

Spectral mixture for remote sensinglinear model and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Spectral mixture for remote sensingby Yosio Edemir Shimabukuro, Flavio Jorge Ponzoni.
其他題名:	linear model and applications /
作者:	Shimabukuro, Yosio Edemir.
其他作者:	Ponzoni, Flavio Jorge.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xiii, 80 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Remote sensingMathematical models.
電子資源:	https://doi.org/10.1007/978-3-030-02017-0
ISBN:	9783030020170$q(electronic bk.)

Spectral mixture for remote sensinglinear model and applications /
Shimabukuro, Yosio Edemir.

Spectral mixture for remote sensinglinear model and applications /[electronic resource] :by Yosio Edemir Shimabukuro, Flavio Jorge Ponzoni. - Cham :Springer International Publishing :2019. - xiii, 80 p. :ill. (some col.), digital ;24 cm. - Springer remote sensing/photogrammetry,2198-0721. - Springer remote sensing/photogrammetry..

Chapter1: Basic concepts -- Chapter2: The origin of digital numbers (DN) -- Chapter3: Orbital sensors -- Chapter4: Linear spectral mixing model -- Chapter5: Fraction images -- Chapter6: Applications of fraction images -- Chapter7: Final considerations.

This book explains in a didactic way the basic concepts of spectral mixing, digital numbers and orbital sensors, and then presents the linear modeling technique of spectral mixing and the generation of fractional images. In addition to presenting a theoretical basis for spectral mixing, the book provides examples of practical applications such as projects for estimating and monitoring deforested areas in the Amazon region. In its seven chapters, the book offers remote sensing techniques to understand the main concepts, methods, and limitations of spectral mixing for digital image processing. Chapter 1 addresses the basic concepts of spectral mixing, while chapters 2 and 3 discuss digital numbers and orbital sensors such as MODIS and Landsat MSS. Chapter 4 details the linear spectral mixing model, and chapter 5 explains the use of this technique to create fraction images. Chapter 6 offers remote sensing applications of fraction images in deforestation monitoring, burned-area mapping, selective logging detection, and land-use/land-cover mapping. Chapter 7 gives some concluding thoughts on spectral mixing, and considers future uses in environmental remote sensing. This book will be of interest to students, teachers, and researchers using remote sensing for Earth observation and environmental modeling.

ISBN: 9783030020170$q(electronic bk.)

Standard No.: 10.1007/978-3-030-02017-0doiSubjects--Topical Terms:

276449
Remote sensing
--Mathematical models.

LC Class. No.: G70.4 / .S556 2019

Dewey Class. No.: 621.3670285

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520 $a This book explains in a didactic way the basic concepts of spectral mixing, digital numbers and orbital sensors, and then presents the linear modeling technique of spectral mixing and the generation of fractional images. In addition to presenting a theoretical basis for spectral mixing, the book provides examples of practical applications such as projects for estimating and monitoring deforested areas in the Amazon region. In its seven chapters, the book offers remote sensing techniques to understand the main concepts, methods, and limitations of spectral mixing for digital image processing. Chapter 1 addresses the basic concepts of spectral mixing, while chapters 2 and 3 discuss digital numbers and orbital sensors such as MODIS and Landsat MSS. Chapter 4 details the linear spectral mixing model, and chapter 5 explains the use of this technique to create fraction images. Chapter 6 offers remote sensing applications of fraction images in deforestation monitoring, burned-area mapping, selective logging detection, and land-use/land-cover mapping. Chapter 7 gives some concluding thoughts on spectral mixing, and considers future uses in environmental remote sensing. This book will be of interest to students, teachers, and researchers using remote sensing for Earth observation and environmental modeling.
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