Applied Optics,
Год журнала:
2024,
Номер
63(21), С. 5768 - 5768
Опубликована: Июль 3, 2024
Compared
to
push-scan
hyperspectral
imagers,
snapshot
imagers
offer
an
advantage
by
minimizing
sensitivity
attitude
jitter
in
underwater
mobile
platforms.
Here
we
present
the
optical
design
and
development
of
microlens
array
integral
field
imager.
The
system
comprises
a
panchromatic
imaging
channel
with
high
spatial
resolution
spectral
lower
resolution.
Through
fusion
high-resolution
images
low-resolution
images,
achieve
images.
Both
share
common
front
objective,
featuring
25
mm
focal
length
wide
36°
view
angle.
Utilizing
prism
dispersion,
spans
band
range
from
465
700
nm
less
than
10
nm.
Specialized
algorithms
for
image
reconstruction
have
been
developed.
experimental
results
across
diverse
scenes
confirm
exemplary
performance
system,
positioning
it
as
robust
solution
imaging.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,
Год журнала:
2024,
Номер
17, С. 5920 - 5945
Опубликована: Янв. 1, 2024
Agriculture
can
be
regarded
as
the
backbone
of
human
civilization.
As
technology
evolved,
synergy
between
agriculture
and
remote
sensing
has
brought
about
a
paradigm
shift,
thereby
entirely
revolutionizing
traditional
agricultural
practices.
Nevertheless,
adoption
technologies
in
face
various
challenges
terms
limited
spatial
temporal
coverage,
high
cloud
cover,
low
data
quality
so
on.
Industry
5.0
marks
new
era
industrial
revolution,
where
humans
machines
collaborate
closely,
leveraging
their
distinct
capabilities,
enhancing
decision
making
sustainability
resilience.
This
paper
provides
comprehensive
survey
on
related
aspects
dealing
with
practices
(I5.0)
era.
We
also
elaborately
discuss
applications
pertaining
to
I5.0-
enabled
for
agriculture.
Finally,
we
several
issues
integration
I5.0
sensing.
offers
valuable
insights
into
current
state,
challenges,
potential
advancements
principles
agriculture,
thus
paving
way
future
research,
development,
implementation
strategies
this
domain.
IEEE Geoscience and Remote Sensing Letters,
Год журнала:
2024,
Номер
21, С. 1 - 5
Опубликована: Янв. 1, 2024
Transformers
have
proven
effective
for
Hyperspectral
Image
Classification
(HSIC)
but
often
incorporate
average
pooling
that
results
in
information
loss.
This
paper
presents
WaveFormer,
a
novel
transformer-based
approach
leverages
wavelet
transforms
invertible
downsampling.
preserves
data
integrity
while
enabling
attention
learning.
Specifically,
WaveFormer
unifies
downsampling
with
to
decompress
feature
maps
without
provides
an
efficient
tradeoff
between
performance
and
computation.
Furthermore,
the
decomposition
enhances
interaction
structural
shape
image
patches
channel
maps.
To
evaluate
we
conducted
extensive
experiments
on
two
benchmark
hyperspectral
datasets.
Our
demonstrate
achieves
state-of-the-art
classification
accuracy,
obtaining
overall
accuracies
of
95.66%
96.54%
Pavia
University
Houston
datasets,
respectively.
By
integrating
transforms,
new
transformer
architecture
imagery
superior
loss
from
pooling.
Environmental Sciences Europe,
Год журнала:
2024,
Номер
36(1)
Опубликована: Апрель 24, 2024
Abstract
Land
use
and
land
cover
(LULC)
analysis
is
crucial
for
understanding
societal
development
assessing
changes
during
the
Anthropocene
era.
Conventional
LULC
mapping
faces
challenges
in
capturing
under
cloud
limited
ground
truth
data.
To
enhance
accuracy
comprehensiveness
of
descriptions
changes,
this
investigation
employed
a
combination
advanced
techniques.
Specifically,
multitemporal
30
m
resolution
Landsat-8
satellite
imagery
was
utilized,
addition
to
computing
capabilities
Google
Earth
Engine
(GEE)
platform.
Additionally,
study
incorporated
random
forest
(RF)
algorithm.
This
aimed
generate
continuous
maps
2014
2020
Shrirampur
area
Maharashtra,
India.
A
novel
multiple
composite
RF
approach
based
on
classification
utilized
final
utilizing
RF-50
RF-100
tree
models.
Both
models
seven
input
bands
(B1
B7)
as
dataset
classification.
By
incorporating
these
bands,
were
able
influence
spectral
information
captured
by
each
band
classify
categories
accurately.
The
inclusion
enhanced
discrimination
classifiers,
increasing
assessment
classes.
indicated
that
exhibited
higher
training
validation/testing
(0.99
0.79/0.80,
respectively).
further
revealed
agricultural
land,
built-up
water
bodies
have
changed
adequately
undergone
substantial
variation
among
classes
area.
Overall,
research
provides
insights
into
application
machine
learning
(ML)
emphasizes
importance
selecting
optimal
enhancing
reliability
GEE
different
present
enabled
interpretation
pixel-level
interactions
while
improving
image
suggested
best
through
identification
Global Sustainability Research,
Год журнала:
2024,
Номер
3(1), С. 1 - 24
Опубликована: Янв. 7, 2024
The
aim
of
this
study
was
to
consolidate
current
information
on
the
utilization
Geographic
Information
Systems
(GIS)
and
Remote
Sensing
(RS)
in
agricultural
sector,
with
a
focus
their
role
promoting
evidence-based
policies
practices
enhance
sustainability.
Additionally,
review
sought
identify
challenges
hindering
widespread
adoption
GIS
RS
applications,
particularly
low-
middle-income
nations.
This
employed
methodology
systematic
literature
review.
findings
indicate
that
technology
sector
has
experienced
notable
increase
over
past
few
years.
primary
areas
use
for
have
been
identified
encompass
crop
yield
estimation,
assessment
soil
fertility,
monitoring
cropping
patterns,
evaluation
drought
conditions,
detection
management
pests
diseases,
implementation
precision
agriculture
techniques,
fertilizer
weed
control.
possesses
capacity
augment
sustainability
by
incorporating
spatial
aspect
into
policies.
Furthermore,
potential
facilitating
decision
making
is
expanding.
Given
escalating
peril
climate
change
food
security,
there
exists
heightened
imperative
include
policy
formulation
decision-making
processes
practices.
might
be
beneficial
informing
development
effectively
integrate
sustainable
climate-smart
agriculture.
Remote Sensing,
Год журнала:
2025,
Номер
17(2), С. 279 - 279
Опубликована: Янв. 15, 2025
The
potential
of
precision
agriculture
(PA)
in
forage
and
grassland
management
should
be
more
extensively
exploited
to
meet
the
increasing
global
food
demand
on
a
sustainable
basis.
Monitoring
biomass
yield
quality
traits
directly
impacts
fertilization
irrigation
practises
frequency
utilization
(cuts)
grasslands.
Therefore,
main
goal
review
is
examine
techniques
for
using
PA
applications
monitor
productivity
To
achieve
this,
authors
discuss
several
monitoring
technologies
plant
stand
characteristics
(including
quality)
that
make
it
possible
adopt
digital
farming
forages
management.
provides
an
overview
about
mass
flow
impact
sensors,
moisture
remote
sensing-based
approaches,
near-infrared
(NIR)
spectroscopy,
mapping
field
heterogeneity
promotes
decision
support
systems
(DSSs)
this
field.
At
small
scale,
advanced
sensors
such
as
optical,
thermal,
radar
mountable
drones;
LiDAR
(Light
Detection
Ranging);
hyperspectral
imaging
can
used
assessing
soil
characteristics.
larger
we
coupling
sensing
with
weather
data
(synergistic
modelling),
Sentinel-2
radiative
transfer
modelling
(RTM),
Sentinel-1
backscatter,
Catboost–machine
learning
methods
terms
harvesting
site-specific
decisions.
It
known
delineation
sward
difficult
mixed
grasslands
due
spectral
similarity
among
species.
Thanks
Diversity-Interactions
models,
jointly
various
species
interactions
under
allowed.
Further,
understanding
complex
might
feasible
by
integrating
un-mixing
super-pixel
segmentation
technique,
multi-level
fusion
procedure,
combined
NIR
spectroscopy
neural
network
models.
This
offers
option
enhancing
implementing
recommend
future
research
direction
inclusion
costs
economic
returns
fodder
production.
