IEEE Geoscience and Remote Sensing Magazine - December 2023 - 46

domains: MARMIT-1 and MARMIT-2 for soil, the Microphytobenthos
Optical Model (MPBOM) for algae and
bacteria biofilm, PROSPECT for plant leaves, Farrell for
fruits and vegetables, and Hapke for solar system objects.
On the one hand, this article provides an overview of the
different works in the literature. On the other hand, it proposes
a classification tree of the different models, allowing
one to know the similarities and differences of the different
models. Ultimately, the aim is to assist researchers
in selecting an appropriate model based on their specific
targeted application.
A REMARKABLE ASSET OF
HSI IN COMPARISON TO MSI
IS THE POSSIBILITY OF
INVERTING PHYSICS-BASED
MODELS WITH A HIGH
LEVEL OF ACCURACY.
INTRODUCTION
In recent years, HSI has been recognized as a very promising
tool for analysis performed in research, control, and
industry [1]. In an image of a given object acquired with
HSI, each pixel contains the reflectance and/or transmittance
spectra of its corresponding
components in an
infinitesimal area in a large
number of continuous hyperthin
spectral channels covering
a wide range of wavelengths
[usually extending
from the visible wavelengths
to near-infrared (NIR) wavelengths]
instead of simply
containing the reflectance
and/or transmittance intensity
in the three traditional broad channels located around
the fundamental colors: red, green, and blue (as is the case
in conventional imagery).
The main benefit of HSI consists of the possibility of
retrieving, by inverse problem-solving, the chemical constituent
contents and biophysical parameter values of each
pixel of the sample under consideration from its contained
hyperspectral reflectance and/or transmittance signal
(which we call the hyperspectral signature). The advantages of
HSI as compared to traditional methods include minimal
sample preparation, nondestructive characterization, fast
acquisition time, and the simultaneous visualization of the
spatial distributions of numerous chemical constituents
and biophysical parameters.
Besides HSI, there exists the much simpler multispectral
imaging (MSI), which consists of the acquisition of light in
a small number of spectral bands (usually in a number between
five and 10) outside and inside the visible spectrum
and shares all the aforementioned HSI advantages. Although
MSI is sufficient for numerous applications [2], [3], [4], [5],
[6], [7], an even larger number of applications require HSI,
particularly in the following areas:
◗ Vegetation and water resource control: For example, this includes
the assessment of water quality by determining the
concentrations of total suspended matter, dissolved organic
matter, chlorophyll content [8], [9], and total phosphorus
[10].
46
◗ Food quality and safety control: For example, this includes the
detection, in apples, of bitter pit defects, which are not visible
to the naked eye, by using an NIR hyperspectral reflectance
system with a spectral range of 900-1,700 nm [11];
the assessment of the quality of strawberries using a visible/
NIR HSI system, which predicts various parameters, such
as moisture content, total soluble solids content, and pH
[12]; and the determination of the ripeness level in tomatoes
with an HSI system functioning in the spectral region
of 396-736 nm [13].
◗ Biomedicine: For example, this includes cancer diagnosis
in the breast, cervix, and skin. In the case of skin cancer
diagnosis, precancerous tissues often exhibit increased
blood flow compared to healthy tissue, which can be detected
through the HSI determination of the concentration
of oxyhemoglobin (Hb)O2
(deoxy-Hb ) [14].
and deoxyhemoglobin
◗ Ecology: For example, this includes the study of the
phytoplankton ecology in a dynamic coastal upwelling
environment through the Hyperspectral Imager for
the Coastal Ocean (HICO) with an enhanced spatial
and spectral resolution to quantify the inherent optical
properties of these phytoplankton [15]; the detection of
air pollution using an NIR domain system to quantify
the carbon content in the atmosphere [16], or using a
visible-domain system to observe the slant column density
of nitrogen dioxide (NO)2
in addition to water vapor
and oxygen dimer present in the atmosphere [17].
◗ Geology of space bodies: For example, this includes the
quantification of the lunar regolith and asteroid minerals
(clinopyroxene, orthopyroxene, plagioclase, olivine, ilmenite,
agglutinate, and volcanic glasses) [18], [19], [20],
[21]; the determination of particle sizes; and the quantification
of the submicroscopic metallic Fe (SMFe) [22].
◗ Atmospheric composition determination for the solar system
objects: For example, this includes the study of Saturn's
Titan's surface-atmosphere interaction by the detection
of methane CH4
and ammonia NH ,3
which are responsible
for changes in the reflectivity properties of an area
and its environment on Titan by using data from the
Cassini visual and infrared mapping spectrometer [23];
the quantification of trace gases and aerosols [as carbon
monoxide (CO) and ozone )]O3
for atmospheric
composition monitoring [24], [25]; and the detection
of volcanic ash in clouds by measuring their heights,
thicknesses, and concentrations [26], [27], [28].
HSI excels in applications requiring the precise detection
of tiny signal variations over a continuous spectrum.
These subtle signals can go unnoticed in MSI systems [29],
[30]. A remarkable asset of HSI in comparison to MSI is
the possibility of inverting physics-based models with a
high level of accuracy. Indeed, currently, apart from certain
exceptions [6], [7], most of the literature on physics-based
model inversions is performed with hyperspectral data and
not multispectral data. Indeed, in most cases, multispectral
data do not contain enough information for an accurate
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE DECEMBER 2023
IEEE Geoscience and Remote Sensing Magazine - December 2023

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