Non-Linear Spectral Unmixing of Hyperspectral Data

Non-Linear Spectral Unmixing of Hyperspectral Data

This book is based on satellite image processing focussing on the potential of hyperspectral image processing research taking a case study-based approach. It covers the background, objectives, and practical issues related to HIP and discusses the needs/potentials of said technology for discriminatio...

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Detalles Bibliográficos
Otros Autores: Chakravortty, Somdatta, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton, FL : CRC Press [2025]
Edición:First edition
Materias:
Hyperspectral imaging.
Artificial satellites in remote sensing.
Image processing > Digital techniques.
Nonlinear optics.
Optical detectors.
Mangrove forests > Sundarbans (Bangladesh and India) > Remote sensing > Case studies.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009869105406719
Tabla de Contenidos:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Table of Contents
  • Preface
  • About the Author
  • 1 Introduction
  • 1.1 Hyperspectral Image Processing
  • 1.2 Availability of Hyperspectral Data
  • 1.3 The Objective of the Book
  • 1.4 Content and Organization of the Book
  • 1.5 Societal Relevance
  • 1.6 A Case Study of Mangrove Endmembers for Result Analysis
  • 2 Hyperspectral Image Processing: A Review
  • 2.1 Existing Multispectral Technology and Its Limitations
  • 2.2 Potentials of Hyperspectral Remote Sensing
  • 2.3 Automated Endmember Detection
  • 2.4 Spectral Mixture Analysis
  • 2.4.1 Linear Spectral Unmixing On Hyperspectral Data
  • 2.4.2 Non-Linear Spectral Unmixing of Hyperspectral Data
  • 3 Preprocessing of Data
  • 3.1 Preprocessing of Data
  • 3.2 Ground Survey
  • 3.2.1 Case Study-Based Ground Truth Results
  • 3.3 Spectral Library Development From Ground Data
  • 3.3.1 Case Study-Based Results
  • 3.4 Preprocessing of Hyperspectral Data
  • 3.4.1 Acquisition of Hyperspectral Data
  • 3.4.2 Bands and Spectral Ranges for Hyperspectral Data
  • 3.4.3 Removal of Absorption Bands and Bands Having No Information
  • 3.4.4 Removal of Bad Columns and Vertical Stripes
  • 3.5 Atmospheric Correction
  • 3.5.1 Analysis of Atmospheric Correction Results
  • 3.6 Geometric Correction
  • 3.6.1 Image-To-Image Registration
  • 3.6.2 Spatial Interpolation Using Coordinate Transformations
  • 3.6.3 Intensity Interpolation and Resampling
  • 3.6.4 Accuracy Assessment of Ground Control Points
  • 3.6.5 Analysis of Geometric Correction Results
  • 4 Endmember Detection
  • 4.1 Automated Endmember Detection Algorithms
  • 4.1.1 Automated Target Generation Process (ATGP)
  • 4.1.2 N-FINDER
  • 4.1.3 Pixel Purity Index (PPI)
  • 4.2 Spectral Signature Analysis of Extracted Endmembers
  • 4.3 Experimental Results of Analysis Between Endmember Detection Algorithms.
  • 5 Least-Squares-Based Linear Spectral Unmixing For Pure Endmembers
  • 5.1 Linear Spectral Unmixing
  • 5.2 Linear Spectral Unmixing of Endmembers: Case Study of Mangrove Ecosystem
  • 5.3 Case Study of Fractional Abundance Estimation of Mangrove Species
  • 5.3.1 Endmember Detection
  • 5.3.2 Fractional Abundance
  • 5.3.3 Accuracy Assessment
  • 6 Non-Linear Unmixing for Classification of Mixed Endmembers
  • 6.1 Limitations of Linear Spectral Unmixing
  • 6.2 Non-Linear Unmixing Models
  • 6.3 Nascimento's Bilinear Spectral Unmixing Model
  • 6.4 Fan's Bilinear Unmixing Model
  • 6.5 Hapke's Bidirectional Model
  • 6.6 Higher-Order Non-Linear Spectral Unmixing Models
  • 6.7 Experimental Results of Analysis of Lower to Higher-Order Non-Linear Models With Case Studies
  • 7 Fuzzy Logic-Based Non-Linear Spectral Unmixing
  • 7.1 Fuzzy Logic-Based Non-Linear Spectral Unmixing
  • 7.2 Fuzzy C-Means (FCM)
  • 7.3 Possibilistic C-Means (PCM)
  • 7.4 Entropy-Based Fuzzy C-Means Unmixing
  • 7.5 Spatial Fuzzy-Based Unmixing
  • 7.6 Applications of Fuzzy-Based Non-Linear Unmixing
  • 8 Machine Learning Models for Classification of Hyperspectral Data
  • 8.1 Machine Learning Models for Classification of Hyperspectral Data
  • 8.2 Classification Models
  • 8.3 Prediction Models
  • 8.3.1 Deep Learning
  • 8.4 Merits and Demerits of the Models Through Experimental Analysis
  • 8.5 Role of Machine/Deep Learning in Spatial Big Data Analytics
  • Case Study: Evaluating the Performance of a Convolutional Neural Network (CNN) Model in Classifying Hyperspectral Images in the Andaman and Nicobar Islands
  • 9 Ecodynamic Modeling
  • 9.1 Ecodynamic Modeling
  • 9.2 The Ecodynamic Model
  • 9.2.1 Ecodynamic Model and Time-Series Analyses of Hyperspectral Data
  • 9.2.2 Application of the Ecodynamic Model in Change Detection of Coastal Endmembers.
  • 9.2.3 Application of Ecodynamic Model in Competitive Growth Rate of Coastal Endmembers
  • 9.2.4 Application of the Ecodynamic Model in Competitive Survival Capacity of Coastal Endmembers
  • 9.2.5 Application of the Ecodynamic Model in Pair-Wise Competition of Coastal Endmembers
  • 9.3 Application of Lotka-Volterra Model in Competitive Growth Rate of Species
  • 9.4 Challenges of Hyperspectral Data for Mapping and Classification - Gaps and Solutions
  • Bibliography
  • Index.

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