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Review
. 2024 Jun 18;10(12):e33208.
doi: 10.1016/j.heliyon.2024.e33208. eCollection 2024 Jun 30.

Hyperspectral imaging and its applications: A review

Anuja Bhargava  1 , Ashish Sachdeva  2 , Kulbhushan Sharma  3 , Mohammed H Alsharif  4 , Peerapong Uthansakul  5 , Monthippa Uthansakul  5
Affiliations
Review

Hyperspectral imaging and its applications: A review

Anuja Bhargava et al. Heliyon. .

Abstract

Hyperspectral imaging has emerged as an effective powerful tool in plentiful military, environmental, and civil applications over the last three decades. The modern remote sensing approaches are adequate for covering huge earth surfaces with phenomenal temporal, spectral, and spatial resolutions. These features make HSI more effective in various applications of remote sensing depending upon the physical estimation of identical material identification and manifold composite surfaces having accomplished spectral resolutions. Recently, HSI has attained immense significance in the research on safety and quality assessment of food, medical analysis, and agriculture applications. This review focuses on HSI fundamentals and its applications like safety and quality assessment of food, medical analysis, agriculture, water resources, plant stress identification, weed & crop discrimination, and flood management. Various investigators have promising solutions for automatic systems depending upon HSI. Future research may use this review as a baseline and future advancement analysis.

Keywords: Hyperspectral; Imaging; Sensors; Spatial; Spectral; Temporal.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Comparison between hypercube and RGB image [2].
Fig. 2
Fig. 2
Three approaches are used for constructing a hyperspectral image (a) Point Scanning (b) Line Scanning (c) Imagers Staring (d) Imager Snapshot [2].
Fig. 3
Fig. 3
Detailed hyperspectral three-dimensional data cube structure [12].
Fig. 4
Fig. 4
A collective scheme of Soil, Water & Vegetation mapping on hyperspectral imaging [27].
Fig. 5
Fig. 5
Soil, Water & Vegetation curve for spectral response [27].
Fig. 6
Fig. 6
Hyperspectral Imaging scanning [58].
Fig. 7
Fig. 7
Grayscale representation of melanoma lesion and internal area with transmission spectra [63].
Fig. 8
Fig. 8
(a) Photographic image of biliary (b)Classification of biliary based on hyperspectral imaging [64].
Fig. 9
Fig. 9
Spectral resolution of healthy/defective sugar leaves [87].
Fig. 10
Fig. 10
Spectral parameters with matured classification [87].
Fig. 11
Fig. 11
Suspended concentration of Bay Meiliang in Taihu Chris Lake [117].
See this image and copyright information in PMC

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