NON-LINEAR SPECTRAL UNMIXING OF HYPERSPECTRAL DATA:
This book is based on satellite image processing, focusing on the potential of hyperspectral image processing (HIP) research with a case study-based approach. It covers the background, objectives, and practical issues related to HIP and substantiates the needs and potentials of said technology for d...
Gespeichert in:
| Beteilige Person: | |
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| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[Erscheinungsort nicht ermittelbar]
CRC PRESS
2024
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| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781040112618/?ar |
| Zusammenfassung: | This book is based on satellite image processing, focusing on the potential of hyperspectral image processing (HIP) research with a case study-based approach. It covers the background, objectives, and practical issues related to HIP and substantiates the needs and potentials of said technology for discrimination of pure and mixed endmembers in pixels, including unsupervised target detection algorithms for extraction of unknown spectra of pure pixels. It includes application of machine learning and deep learning models on hyperspectral data and its role in spatial big data analytics. Features include the following: Focuses on capability of hyperspectral data in characterization of linear and non-linear interactions of a natural forest biome. Illustrates modeling the ecodynamics of mangrove habitats in the coastal ecosystem. Discusses adoption of appropriate technique for handling spatial data (with coarse resolution). Covers machine learning and deep learning models for classification. Implements non-linear spectral unmixing for identifying fractional abundance of diverse mangrove species of coastal Sundarbans. This book is aimed at researchers and graduate students in digital image processing, big data, and spatial informatics. |
| Umfang: | 1 Online-Ressource |
| ISBN: | 9781040112557 1040112552 9781003432623 100343262X 9781040112618 1040112617 |
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| 520 | |a This book is based on satellite image processing, focusing on the potential of hyperspectral image processing (HIP) research with a case study-based approach. It covers the background, objectives, and practical issues related to HIP and substantiates the needs and potentials of said technology for discrimination of pure and mixed endmembers in pixels, including unsupervised target detection algorithms for extraction of unknown spectra of pure pixels. It includes application of machine learning and deep learning models on hyperspectral data and its role in spatial big data analytics. Features include the following: Focuses on capability of hyperspectral data in characterization of linear and non-linear interactions of a natural forest biome. Illustrates modeling the ecodynamics of mangrove habitats in the coastal ecosystem. Discusses adoption of appropriate technique for handling spatial data (with coarse resolution). Covers machine learning and deep learning models for classification. Implements non-linear spectral unmixing for identifying fractional abundance of diverse mangrove species of coastal Sundarbans. This book is aimed at researchers and graduate students in digital image processing, big data, and spatial informatics. | ||
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| spelling | Chakravortty, Somdatta VerfasserIn aut NON-LINEAR SPECTRAL UNMIXING OF HYPERSPECTRAL DATA [Erscheinungsort nicht ermittelbar] CRC PRESS 2024 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This book is based on satellite image processing, focusing on the potential of hyperspectral image processing (HIP) research with a case study-based approach. It covers the background, objectives, and practical issues related to HIP and substantiates the needs and potentials of said technology for discrimination of pure and mixed endmembers in pixels, including unsupervised target detection algorithms for extraction of unknown spectra of pure pixels. It includes application of machine learning and deep learning models on hyperspectral data and its role in spatial big data analytics. Features include the following: Focuses on capability of hyperspectral data in characterization of linear and non-linear interactions of a natural forest biome. Illustrates modeling the ecodynamics of mangrove habitats in the coastal ecosystem. Discusses adoption of appropriate technique for handling spatial data (with coarse resolution). Covers machine learning and deep learning models for classification. Implements non-linear spectral unmixing for identifying fractional abundance of diverse mangrove species of coastal Sundarbans. This book is aimed at researchers and graduate students in digital image processing, big data, and spatial informatics. Hyperspectral imaging Artificial satellites in remote sensing Image processing Digital techniques Nonlinear optics Optical detectors Mangrove forests Case studies Remote sensing Sundarbans (Bangladesh and India) Satellites artificiels en télédétection Traitement d'images ; Techniques numériques Optique non linéaire Détecteurs optiques Mangroves ; Sundarbans (Bangladesh et Inde) ; Télédétection ; Études de cas digital imaging TECHNOLOGY / Environmental Engineering & Technology 1032450495 Erscheint auch als Druck-Ausgabe 1032450495 |
| spellingShingle | Chakravortty, Somdatta NON-LINEAR SPECTRAL UNMIXING OF HYPERSPECTRAL DATA Hyperspectral imaging Artificial satellites in remote sensing Image processing Digital techniques Nonlinear optics Optical detectors Mangrove forests Case studies Remote sensing Sundarbans (Bangladesh and India) Satellites artificiels en télédétection Traitement d'images ; Techniques numériques Optique non linéaire Détecteurs optiques Mangroves ; Sundarbans (Bangladesh et Inde) ; Télédétection ; Études de cas digital imaging TECHNOLOGY / Environmental Engineering & Technology |
| title | NON-LINEAR SPECTRAL UNMIXING OF HYPERSPECTRAL DATA |
| title_auth | NON-LINEAR SPECTRAL UNMIXING OF HYPERSPECTRAL DATA |
| title_exact_search | NON-LINEAR SPECTRAL UNMIXING OF HYPERSPECTRAL DATA |
| title_full | NON-LINEAR SPECTRAL UNMIXING OF HYPERSPECTRAL DATA |
| title_fullStr | NON-LINEAR SPECTRAL UNMIXING OF HYPERSPECTRAL DATA |
| title_full_unstemmed | NON-LINEAR SPECTRAL UNMIXING OF HYPERSPECTRAL DATA |
| title_short | NON-LINEAR SPECTRAL UNMIXING OF HYPERSPECTRAL DATA |
| title_sort | non linear spectral unmixing of hyperspectral data |
| topic | Hyperspectral imaging Artificial satellites in remote sensing Image processing Digital techniques Nonlinear optics Optical detectors Mangrove forests Case studies Remote sensing Sundarbans (Bangladesh and India) Satellites artificiels en télédétection Traitement d'images ; Techniques numériques Optique non linéaire Détecteurs optiques Mangroves ; Sundarbans (Bangladesh et Inde) ; Télédétection ; Études de cas digital imaging TECHNOLOGY / Environmental Engineering & Technology |
| topic_facet | Hyperspectral imaging Artificial satellites in remote sensing Image processing Digital techniques Nonlinear optics Optical detectors Mangrove forests Case studies Remote sensing Sundarbans (Bangladesh and India) Satellites artificiels en télédétection Traitement d'images ; Techniques numériques Optique non linéaire Détecteurs optiques Mangroves ; Sundarbans (Bangladesh et Inde) ; Télédétection ; Études de cas digital imaging TECHNOLOGY / Environmental Engineering & Technology |
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