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.
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