Isbn: 9781962116732 - multiscale spectral-spatial feature learning for hyperspectral image classification (8 resultados)

Condición: New.

Paperback. Condición: Brand New. 142 pages. 6.00x0.30x9.00 inches. In Stock.

Paperback. Condición: new. Paperback. Hyperspectral imaging provides detailed spectral information across numerous wavelength bands, enabling the analysis and classification of materials and objects that may appear visually similar in conventional imagery. Multiscale Spectral-Spatial Feature Learning for Hyperspectral Image Classification presents a focused technical treatment of spectral-spatial representation, multiscale feature extraction, machine learning, and classification methods for hyperspectral image data. The book connects hyperspectral imaging, digital image processing, pattern recognition, computer vision, feature learning, and machine learning within a structured computational framework.The book introduces the fundamental characteristics of hyperspectral imagery and explains the importance of exploiting both spectral and spatial information during image analysis. Unlike conventional images, hyperspectral data contain rich spectral signatures that can provide detailed information about the materials represented in individual pixels. At the same time, neighboring pixels contain spatial relationships that can contribute valuable contextual information. Understanding the complementary roles of spectral and spatial information is therefore central to effective hyperspectral image classification.A central focus is placed on multiscale feature learning. Different spatial scales can capture structures ranging from fine local patterns to broader contextual regions, while spectral representations can distinguish materials based on their wavelength-dependent characteristics. The book examines how these complementary forms of information can be represented, combined, and learned to produce discriminative features for classification tasks. Multiscale Spectral-Spatial Feature Learning for Hyperspectral Image Classification explores computational methods for extracting and learning spectral and spatial representations from hyperspectral imagery. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Paperback. Condición: new. Paperback. Hyperspectral imaging provides detailed spectral information across numerous wavelength bands, enabling the analysis and classification of materials and objects that may appear visually similar in conventional imagery. Multiscale Spectral-Spatial Feature Learning for Hyperspectral Image Classification presents a focused technical treatment of spectral-spatial representation, multiscale feature extraction, machine learning, and classification methods for hyperspectral image data. The book connects hyperspectral imaging, digital image processing, pattern recognition, computer vision, feature learning, and machine learning within a structured computational framework.The book introduces the fundamental characteristics of hyperspectral imagery and explains the importance of exploiting both spectral and spatial information during image analysis. Unlike conventional images, hyperspectral data contain rich spectral signatures that can provide detailed information about the materials represented in individual pixels. At the same time, neighboring pixels contain spatial relationships that can contribute valuable contextual information. Understanding the complementary roles of spectral and spatial information is therefore central to effective hyperspectral image classification.A central focus is placed on multiscale feature learning. Different spatial scales can capture structures ranging from fine local patterns to broader contextual regions, while spectral representations can distinguish materials based on their wavelength-dependent characteristics. The book examines how these complementary forms of information can be represented, combined, and learned to produce discriminative features for classification tasks. Multiscale Spectral-Spatial Feature Learning for Hyperspectral Image Classification explores computational methods for extracting and learning spectral and spatial representations from hyperspectral imagery. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condición: new. Paperback. Hyperspectral imaging provides detailed spectral information across numerous wavelength bands, enabling the analysis and classification of materials and objects that may appear visually similar in conventional imagery. Multiscale Spectral-Spatial Feature Learning for Hyperspectral Image Classification presents a focused technical treatment of spectral-spatial representation, multiscale feature extraction, machine learning, and classification methods for hyperspectral image data. The book connects hyperspectral imaging, digital image processing, pattern recognition, computer vision, feature learning, and machine learning within a structured computational framework.The book introduces the fundamental characteristics of hyperspectral imagery and explains the importance of exploiting both spectral and spatial information during image analysis. Unlike conventional images, hyperspectral data contain rich spectral signatures that can provide detailed information about the materials represented in individual pixels. At the same time, neighboring pixels contain spatial relationships that can contribute valuable contextual information. Understanding the complementary roles of spectral and spatial information is therefore central to effective hyperspectral image classification.A central focus is placed on multiscale feature learning. Different spatial scales can capture structures ranging from fine local patterns to broader contextual regions, while spectral representations can distinguish materials based on their wavelength-dependent characteristics. The book examines how these complementary forms of information can be represented, combined, and learned to produce discriminative features for classification tasks. Multiscale Spectral-Spatial Feature Learning for Hyperspectral Image Classification explores computational methods for extracting and learning spectral and spatial representations from hyperspectral imagery. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Hyperspectral imaging provides detailed spectral information across numerous wavelength bands, enabling the analysis and classification of materials and objects that may appear visually similar in conventional imagery. Multiscale Spectral-Spatial Feature Learning for Hyperspectral Image Classification presents a focused technical treatment of spectral-spatial representation, multiscale feature extraction, machine learning, and classification methods for hyperspectral image data. The book connects hyperspectral imaging, digital image processing, pattern recognition, computer vision, feature learning, and machine learning within a structured computational framework.The book introduces the fundamental characteristics of hyperspectral imagery and explains the importance of exploiting both spectral and spatial information during image analysis. Unlike conventional images, hyperspectral data contain rich spectral signatures that can provide detailed information about the materials represented in individual pixels. At the same time, neighboring pixels contain spatial relationships that can contribute valuable contextual information. Understanding the complementary roles of spectral and spatial information is therefore central to effective hyperspectral image classification.A central focus is placed on multiscale feature learning. Different spatial scales can capture structures ranging from fine local patterns to broader contextual regions, while spectral representations can distinguish materials based on their wavelength-dependent characteristics. The book examines how these complementary forms of information can be represented, combined, and learned to produce discriminative features for classification tasks.…

Taschenbuch. Condición: Neu. Multiscale Spectral-Spatial Feature Learning for Hyperspectral Image Classification | Newton | Taschenbuch | Englisch | 2026 | SHARK NAIL | EAN 9781962116732 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…