IEEE Geoscience and Remote Sensing Magazine - June 2021 - 57
constraints ,C which can be generalized by optimizing the
following minimization problem:
mi () ..s t
X
n f2
1
XX ,C
!
(2)
where X is the to-be-estimated variable, which can be defined
as a vector (1D signal), a matrix (2D image), or an
unfolded matrix (3D HS image). The constraints C could
be sparsity-promoting variants, low rank, total variation
(TV), and others, which need to be determined by the
specific tasks.
Unlike convex models, there exist many local minimums
in nonconvex models. This poses a big challenge
for finding globally optimal solutions. For possible solutions
to nonconvex methods, one strategy is to relax the
Image Restoration
DR
Solar (Lighting Source)
Passive RS
Optimal Dimension
Curse of Dimensionality
HS Satellite (or Sensor)
Classification
Performance Versus Feature Dimensionality
Forests Rocks Grass
Built-Up Area
Dimensionality (Number of Features)
Noise
Stripe
(a)
Data Fusion and Enhancement
Cloud
Ideal HSI
Ground Truth Subspace Features
(b)
HSI
Wavelength
Wavelength
Mixed Pixel
Pure Pixel
Wavelength
Pure Pixel
30 m
GSD
5 m
GSD
5 m
GSD
HSI
MSI
(c)
FIGURE 3. Five promising topics in HS RS, including (a) image restoration, (b) DR and classification, (c) data fusion and enhancement, (d) SU, and
(e) CML. HSI: HS imaging; MSI: MS imaging; GSD: ground-sampling distance; MML: multimodality learning. (continued)
JUNE 2021 IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
57
Fused Data
Classification Performance
IEEE Geoscience and Remote Sensing Magazine - June 2021
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