Remote Sensing of Environment,
Год журнала:
2024,
Номер
304, С. 114046 - 114046
Опубликована: Фев. 13, 2024
Spatiotemporal
fusion
aims
to
improve
both
the
spatial
and
temporal
resolution
of
remote
sensing
images,
thus
facilitating
time-series
analysis
at
a
fine
scale.
However,
there
are
several
important
issues
that
limit
application
current
spatiotemporal
methods.
First,
most
methods
based
on
pixel-level
computation,
which
neglects
valuable
shape
information
ground
objects.
Moreover,
many
existing
cannot
accurately
retrieve
strong
changes
between
available
high-resolution
image
base
date
predicted
one.
This
study
proposes
an
Object-Based
Spatial
Unmixing
Model
(OBSUM),
incorporates
object-based
unmixing,
overcome
two
abovementioned
problems.
OBSUM
consists
one
preprocessing
step
three
steps,
i.e.,
object-level
residual
compensation,
compensation.
The
performance
was
compared
with
seven
representative
agricultural
sites.
experimental
results
demonstrated
outperformed
other
in
terms
accuracy
indices
visual
effects
over
time-series.
Furthermore,
also
achieved
satisfactory
crop
progress
monitoring
mapping.
Therefore,
it
has
great
potential
generate
accurate
observations
for
supporting
various
applications.
IEEE Transactions on Geoscience and Remote Sensing,
Год журнала:
2024,
Номер
62, С. 1 - 17
Опубликована: Янв. 1, 2024
Leveraging
the
extensive
training
data
from
SA-1B,
Segment
Anything
Model
(SAM)
demonstrates
remarkable
generalization
and
zero-shot
capabilities.
However,
as
a
category-agnostic
instance
segmentation
method,
SAM
heavily
relies
on
prior
manual
guidance,
including
points,
boxes,
coarse-grained
masks.
Furthermore,
its
performance
in
remote
sensing
image
tasks
remains
largely
unexplored
unproven.
In
this
paper,
we
aim
to
develop
an
automated
approach
for
images,
based
foundational
model
incorporating
semantic
category
information.
Drawing
inspiration
prompt
learning,
propose
method
learn
generation
of
appropriate
prompts
SAM.
This
enables
produce
semantically
discernible
results
concept
have
termed
RSPrompter.
We
also
several
ongoing
derivatives
tasks,
drawing
recent
advancements
within
community,
compare
their
with
Extensive
experimental
results,
derived
WHU
building,
NWPU
VHR-10,
SSDD
datasets,
validate
effectiveness
our
proposed
method.
The
code
is
publicly
available
at
https://kychen.me/RSPrompter.
Deleted Journal,
Год журнала:
2024,
Номер
21(4), С. 617 - 630
Опубликована: Апрель 12, 2024
Abstract
Recently,
Meta
AI
Research
approaches
a
general,
promptable
segment
anything
model
(SAM)
pre-trained
on
an
unprecedentedly
large
segmentation
dataset
(SA-1B).
Without
doubt,
the
emergence
of
SAM
will
yield
significant
benefits
for
wide
array
practical
image
applications.
In
this
study,
we
conduct
series
intriguing
investigations
into
performance
across
various
applications,
particularly
in
fields
natural
images,
agriculture,
manufacturing,
remote
sensing
and
healthcare.
We
analyze
discuss
limitations
SAM,
while
also
presenting
outlook
its
future
development
tasks.
By
doing
so,
aim
to
give
comprehensive
understanding
SAM’s
This
work
is
expected
provide
insights
that
facilitate
research
activities
toward
generic
segmentation.
Source
code
publicly
available
at
https://github.com/LiuTingWed/SAM-Not-Perfect
.
IEEE Transactions on Geoscience and Remote Sensing,
Год журнала:
2024,
Номер
62, С. 1 - 11
Опубликована: Янв. 1, 2024
Vision
Foundation
Models
(VFMs)
such
as
the
Segment
Anything
Model
(SAM)
allow
zero-shot
or
interactive
segmentation
of
visual
contents,
thus
they
are
quickly
applied
in
a
variety
scenes.
However,
their
direct
use
many
Remote
Sensing
(RS)
applications
is
often
unsatisfactory
due
to
special
imaging
properties
RS
images.
In
this
work,
we
aim
utilize
strong
recognition
capabilities
VFMs
improve
change
detection
(CD)
very
high-resolution
(VHR)
remote
sensing
images
(RSIs).
We
employ
encoder
FastSAM,
variant
SAM,
extract
representations
To
adapt
FastSAM
focus
on
some
specific
ground
objects
scenes,
propose
convolutional
adaptor
aggregate
task-oriented
information.
Moreover,
semantic
that
inherent
SAM
features,
introduce
task-agnostic
learning
branch
model
latent
bi-temporal
RSIs.
The
resulting
method,
SAM-CD,
obtains
superior
accuracy
compared
SOTA
fully-supervised
CD
methods
and
exhibits
sample-efficient
ability
comparable
semi-supervised
methods.
best
our
knowledge,
first
work
adapts
VHR
Remote Sensing,
Год журнала:
2024,
Номер
16(5), С. 804 - 804
Опубликована: Фев. 25, 2024
Change
detection
(CD)
in
remote
sensing
(RS)
imagery
is
a
pivotal
method
for
detecting
changes
the
Earth’s
surface,
finding
wide
applications
urban
planning,
disaster
management,
and
national
security.
Recently,
deep
learning
(DL)
has
experienced
explosive
growth
and,
with
its
superior
capabilities
feature
pattern
recognition,
it
introduced
innovative
approaches
to
CD.
This
review
explores
latest
techniques,
applications,
challenges
DL-based
CD,
examining
them
through
lens
of
various
paradigms,
including
fully
supervised,
semi-supervised,
weakly
unsupervised.
Initially,
introduces
basic
network
architectures
CD
methods
using
DL.
Then,
provides
comprehensive
analysis
under
different
summarizing
commonly
used
frameworks.
Additionally,
an
overview
publicly
available
datasets
offered.
Finally,
addresses
opportunities
field,
including:
(a)
incomplete
supervised
encompassing
semi-supervised
methods,
which
still
infancy
requires
further
in-depth
investigation;
(b)
potential
self-supervised
learning,
offering
significant
Few-shot
One-shot
Learning
CD;
(c)
development
Foundation
Models,
their
multi-task
adaptability,
providing
new
perspectives
tools
(d)
expansion
data
sources,
presenting
both
multimodal
These
areas
suggest
promising
directions
future
research
In
conclusion,
this
aims
assist
researchers
gaining
understanding
field.
Remote Sensing of Environment,
Год журнала:
2024,
Номер
304, С. 114047 - 114047
Опубликована: Фев. 13, 2024
Small
water
bodies
(<
0.01
km2)
showing
diverse
limnological
properties
occur
in
great
abundance
across
the
boreal
forest
and
tundra
landscapes
of
Arctic
Subarctic.
However,
their
classification,
geographical
distribution
collective
importance
for
water,
heat,
nutrient,
contaminant
carbon
cycles
are
still
poorly
constrained.
One
important
step
better
understanding
role
evolution
small
fast-changing
northern
is
to
develop
image
analysis
protocols
that
allow
automatic
remote
sensing
detection,
delineation
inventory.
In
this
study,
we
set
an
protocol
(High
Latitude
Water
–
HLWATER
V1.0)
based
on
a
trained
supervised
Mask
R-CNN
deep
learning
model
over
PlanetScope
imagery
detection
lakes
ponds
were
absent
existing
datasets.
Most
our
training
dataset
comprised
smaller
than
km2
(97%)
spanned
wide
range
environmental
hydrological
settings,
from
sporadic
continuous
permafrost
zones
Canada.
The
was
tested
as
fully
autonomous
approach
eastern
Hudson
Bay,
Nunavik
(Subarctic
Canada),
region
poses
challenges
given
variety
bodies.
These
mainly
thaw
glacial
basin
forest-tundra
challenging
optical
settings
influenced
by
vegetation
or
topography
shadowing,
revealing
peat
logging,
fen
bog
pond
conditions.
A
multi-scale
validation
developed
using
body
delineations
ultra-high
resolution
orthomosaics
Unoccupied
Aerial
Systems.
This
procedure
allowed
sub-pixel
assessment
identified
limitations
strengths
detecting
large
results
varied
according
different
landscape
units,
with
mean
Intersection
Union
(IoU)
0.5
F1
Scores
0.53
0.71
0.62
0.95.
Considering
166
m2
minimum
size
threshold,
IoU
0.7
0.91
0.76
0.83,
evaluated
comparing
manual
delineations.
show
high
potential
extension
other
regions
Subarctic,
allowing
detailed
inventories
optically
morphologically
areas
circumpolar
North.
Remote Sensing,
Год журнала:
2025,
Номер
17(3), С. 400 - 400
Опубликована: Янв. 24, 2025
Grassland
ecosystems
provide
a
range
of
services
in
semi-arid
and
arid
regions.
However,
they
have
significantly
declined
due
to
overgrazing
desertification.
In
the
current
study,
we
employed
Landsat
time
series
data
(TM,
OLI,
OLI-2)
spanning
from
1990
2024,
combined
with
vegetation
indices
such
as
NDVI
SAVI,
along
NDWI
digital
elevation
models
(DEMs),
analyze
land
cover
dynamics
Ugii
Lake
watershed
area,
Mongolia.
By
integrating
multisource
remote
sensing
into
advanced
XGBoost
(extreme
gradient
boosting)
machine
learning
algorithm,
achieved
high
classification
accuracy,
overall
accuracies
exceeding
94%
Kappa
coefficients
greater
than
0.92.
The
results
revealed
decline
montane
grasslands
(−6.2%)
an
increase
other
grassland
types,
suggesting
ecosystem
redistribution
influenced
by
climatic
anthropogenic
factors.
Cropland
exhibited
resilience,
recovering
significant
1990s
moderate
growth
2024.
Our
findings
highlight
stability
barren
underscore
pressures
ecological
degradation
human
activities.
This
study
provides
up-to-date
statistical
support
decision-making
conservation
sustainable
management
Mongolia
under
changing
conditions.