Critical insights into modern hyperspectral image applications through deep learning

Abstract

Hyperspectral imaging has shown tremendous growth over the past three decades. Hyperspectral imaging was evolved through remote sensing. Along, with the technological enhancements hyperspectral imaging has outgrown, conquering over other various application areas. In addition to it, data enriched data cubes with abundant spectral and spatial information works as perk for capturing, analyzing, reviewing, and interpreting results from data. This review concentrates on emerging application areas of hyperspectral imaging. Emerging application areas are selected in ways where there is a vast scope for future enhancements by exploiting cutting edge technology, that is, deep learning. Applications of hyperspectral imaging techniques in some selected areas (remote sensing, document forgery, history and archaeology conservation, surveillance and security, machine vision for fruit quality inspection, medical imaging) are focused. The review pivots around the publicly available datasets and features used domain wise. This review can act as a baseline for deep learning and machine vision experts, historical geographers, and scholars by providing them a view of how hyperspectral imaging is implemented in multiple domains along with future research prospects. This article is categorized under: Technologies > Machine Learning Technologies > Prediction

mHealth hyperspectral learning for instantaneous spatiospectral imaging of hemodynamics

“…Typically, hyperspectral imaging systems rely on mechanical scanning elements either in the spectral or spatial domains. In particular, spectral scanning systems employ a number of narrow bandpass spectral filters or dispersive optical components, whereas point scanning and line scanning systems rely on mechanical translational components that require high precision ( 1 , 2 , 6 , 7 , 9 ). Thus, these scanning elements result in bulky instruments and yield suboptimal temporal resolutions.…”

“…However, previously developed hyperspectral imaging technologies with a snapshot ability face several limitations ( 1 , 2 , 9 , 46 , 49 ). First, typical snapshot systems are limited by the intrinsic trade-off that must be made between the spectral and spatial resolutions; an improvement in spatial resolution causes a deterioration in the number of spectral bands, thereby compromising the spectral resolution or the spatial resolution (or imaging area).…”

“…Third, the hyperspectral filter arrays, dispersion patterns, and coded apertures require high-precision fabrication or nanofabrication, including precision alignment of array components, optimized miniaturization, integration with pixel-level filters, and customized calibrations, all of which inhibit manufacturability. Consequently, the previous studies have generally been performed under laboratory settings ( 50 , 51 ) or with stationary biological samples ( 7 , 9 , 52 ), thereby hampering the practical and widespread utilization.…”

Perspective Chapter: Hyperspectral Imaging for the Analysis of Seafood

“…Additionally, most of the methods covered in the literature are based exclusively on the spectral information, while the spatial information is usually underrated. However, the trend in hyperspectral image analysis in other fields is to try to exploit simultaneously both the spatial and the spectral features of the data, especially with the rise of sophisticated deep learning architectures to this end [76,77].…”

HSIS-Net: Hyperspectral Image Segmentation Using Multi-view Active Learning Based FCSN

“…This section performs the detailed survey on segmentation approaches. There are three different categories of segmentation approaches are used, pixel-level segmentation [15], object-level segmentation [16], and sub-pixel segmentation or unmixing [17]. The choice of approach depends mainly on the application requirement since they offer both advantages and limitations.…”