Spectral Mixing - Linear Model and Applications

Introduction

Spectral mixing is a fundamental concept in remote sensing that describes how the reflectance spectrum of a pixel in an image is a mixture of the reflectance spectra of the materials present in that pixel. This book provides a comprehensive treatment of spectral mixing, from its theoretical foundations to its practical applications.

Linear Mixing Model

The linear mixing model is the most commonly used model for spectral mixing. It assumes that the reflectance spectrum of a pixel is a linear combination of the reflectance spectra of the endmembers, which are the pure materials present in the pixel. The mixing coefficients represent the proportions of each endmember in the pixel.

Spectral Unmixing

Spectral unmixing is the process of estimating the mixing coefficients for each pixel in an image. This can be done using a variety of techniques, including linear least squares, constrained least squares, and maximum likelihood estimation.

Applications of Spectral Mixing

Spectral mixing has a wide range of applications in remote sensing, including:

• Land cover classification
• Vegetation mapping
• Mineral exploration
• Environmental monitoring
• Disaster response

Why You Should Buy This Book

This book is the most comprehensive treatment of spectral mixing available. It provides a thorough understanding of the theoretical foundations of spectral mixing, as well as practical guidance on how to apply spectral mixing to real-world problems.

This book is essential for anyone who wants to work in the field of remote sensing. It is also a valuable resource for anyone who is interested in learning more about the science of spectral mixing.

Order Your Copy Today!

Don't miss out on this opportunity to learn more about spectral mixing. Order your copy of Spectral Mixing - Linear Model and Applications today!