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## Meet the editor

Jung Y. Huang, a university educator and researcher, has been working to unravel the structures and functional properties of materials and cellular events in living cells with varying optical methodologies. He has co-authored hundreds of journal papers and five book chapters. He also holds tens of patents in laser techniques and single-molecule/hyperspectral imaging and has developed architectural photonics based on hierarchically

structured materials. Currently, his research focuses on the integration of artificial intelligence methodology with optics to automatically discover meaningful information from optical sensing/imaging data cubes. He has been an editorial board member and reviewer for several scientific journals. As a member of the global scientific community, he sincerely supports and endeavors to promote the spread of scientific knowledge.

## Contents


Preface

Hyperspectral imaging (HSI) is an aerial imaging technology that measures the way an object reflects and emits light at different wavelengths. Typically, it can cover hundreds of bands of light in the electromagnetic spectrum, revealing the precise spectral properties of materials found in the region of interest. With the resulting data, the methodology can distinguish the subtle differences between similar objects, allowing it to map out and differentiate objects and materials in great detail. Due to its fine-grained resolution and ability to distinguish different chemical species, HSI is becoming a powerful tool to spatially resolve the chemistry of materials in varying

HSI data acquisition involves the use of an aerial detector multiplexed in two dimensions and, therefore, requires multiple measurements to complete one data acquisition cycle. The multiple measurements can be executed in two different ways, position scanning or wavelength scanning. Position scanning HSI acquires 2D data of one spatial dimension and the spectral dimension, and scans across the other spatial dimension, whereas wavelength scanning HSI multiplexes the two spatial dimensions and scans across the spectral dimension. Clearly, both methods need time to complete a data cube acquisition. Enabling fast HSI will open doors to new applications where multiple constituents or spatiotemporal dynamics need to be resolved. A variety of snapshot techniques have been developed by invoking a spatial-spectral modulation scheme, such as illuminating an object with a coded light

pattern or inserting a spectral modulation module in the HSI imaging device.

HSI is currently applied in many fields. However, we also face a new challenge in data processing and in how to reliably retrieve meaningful information from the highdimensional HSI data cubes in real-time. Recent progress in both machine-learning and deep-learning techniques may offer a solution to this issue. This book brings together a collection of five chapters offering a glimpse of the status of machine- and deep-learning methodological development for hyperspectral imaging applications.

Chapter 1 "Perspective Chapter: Hyperspectral Imaging for the Analysis of Seafood", by Samuel Ortega et al., presents a survey of current uses of hyperspectral technology for seafood evaluation. The authors briefly describe the optical properties of tissue and offer an introduction to the instrumentation and the developmental status of HSI

As noted above, consistent data preprocessing and reliable feature extraction are the first step to meaningful information retrieval from high-dimensional data cubes. Chapter 2, "Useful Feature Extraction and Machine Learning Techniques for Identifying Unique Pattern Signatures Present in Hyperspectral Image Data", by Jeanette Hariharan et al., presents a data preprocessing protocol for HSI data. The authors review feature extraction techniques that are useful for identifying pattern

scientific and engineering disciplines.

in the relevant aspects of the seafood industry.
