Useful Feature Extraction and Machine Learning Techniques for Identifying Unique Pattern Signatures Present in Hyperspectral Image Data
This chapter introduces several feature extraction techniques (FETs) and machine learning algorithms (MLA) that are useful for pattern recognition in hyperspectral data analysis (HDA). This chapter provides a handbook of the most popular FETs that have proven successful. Machine learning algorithms...
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| Main Authors: | , , , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
IntechOpen
2022
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| Subjects: | |
| Online Access: | https://www.intechopen.com/chapters/84595 |
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| Summary: | This chapter introduces several feature extraction techniques (FETs) and machine learning algorithms (MLA) that are useful for pattern recognition in hyperspectral data analysis (HDA). This chapter provides a handbook of the most popular FETs that have proven successful. Machine learning algorithms (MLA) for use with HDA are becoming prevalent in pattern recognition literature. Several of these algorithms are explained in detail to provide the user with insights into applying these for pattern recognition. Unsupervised learning applications are useful when the system is provided with the correct set of independent variables. Various forms of linear regression assay adequately solve hyperspectral pattern resolution for identifying phenotypes. K-means is an unsupervised learning algorithm that is used for systematically dividing a dataset into K number of pattern groups. Supervised and unsupervised neural networks (NNs) are used to discern patterns in hyperspectral data with features as inputs and in large datasets where little a priori knowledge is applied. Other supervised machine learning procedures derive valuable feature detectors and descriptors through support vector machine. Several methods using reduced sets for extracting patterns from hyperspectral data are shown by discretized numerical techniques and transformation processes. The accuracy of these methods and their usefulness is generally assessed. |
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| ISBN: | 978-1-83969-093-8 |
| DOI: | 10.5772/intechopen.107436 |