IEEE Geoscience and Remote Sensing Magazine - June 2021 - 78

False-Color Map
SSMA
O-Baseline
CoSpace
Healthy Grass
Stressed Grass
Synthetic Grass
Trees
Soil
Water
USMA
LeMA
Residential
Commercial
Road
Highway
Railway
SMA
GiAL
Parking Lot 1
Parking Lot 2
Tennis Court
Running Track
FIGURE 14. An region of interest visualization of classification maps using different SOTA
methods related to the CML's issue.
◗ Model transferability: Due to different imaging mechanisms
and principles, the viscosity among pixels from
the same modality is much stronger than it is from the
different modalities. This might lead to difficulties in
fusing multimodal information at a deep level, particularly
heterogeneous data (e.g., HS and SAR data), further
limiting the model's transferability.
◗ Labeling: Unlike natural
images or street-view images
that are relatively easy and accurate to be labeled manually,
labeling RS scenes is extremely expensive and time
consuming (field trips needed). Consequently, a limited
number of labeled samples are available for training,
and, even worse, there are many noisy labels in these
samples. These problems will be noteworthy and to-besolved
key points of next-generation interpretable AI
models in RS-related CML tasks.
CONCLUSIONS AND FUTURE PROSPECTS
Characterized by nearly continuous spectral profiles capable
of sampling and representing the whole electromagnetic
spectrum, HS images play an important role in both
promoting the development of new techniques and accelerating
practical applications related to the fields of RS, geoscience,
signal and image processing, numerical optimization
78
and modeling, ML, and AI. However,
there still exist significant difficulties
and challenges that need to be
carefully considered in the development
and application of HS RS techniques.
One sign reveals that the HS
data analysis methods dominated by
expert systems have been unable to
meet the demands of an ever-growing
volume of HS data, whether in
performance gain or in processing efficiency.
Another sign is that, despite
the current unprecedented progress
made on computer vision, ML, and
AI techniques, the model compatibility
and interpretability for HS RS
applications remains limited.
Due to the SVs of HS data caused
by various degradation mechanisms
(e.g., environmental conditions, atmospheric
effects, spectral nonlinear
mixing, and so on), the redundancy
of high-dimensional HS signals, and
the complex practical cases underlying
HS products (e.g., low-spatial
resolution, narrow imaging range,
and instrumental noises), convex
models under ideal circumstances
usually fail to extract useful diagnostic
information from HS images
(especially those products that are
corrupted seriously) and therefore to
understand our environment. Considering that nonconvex
modeling is capable of characterizing more complex real
scenes and better providing model interpretability, in this
article, we presented a comprehensive and technical survey
of five promising and representative research topics related
to HS RS with a focus on nonconvex modeling, such
as HS image restoration, DR and classification, data fusion
and enhancement, SU, and CML. Among these topics, we
reviewed current SOTA methods with illustrations, showed
the significance and superiority of nonconvex modeling
to bridge the gap between HS RS and interpretable AI,
and pointed out the remaining challenges and future research
directions.
It is well known that the HS image processing and analysis
chain is wide ranging. Apart from the five topics covered in
this article, we were unable to provide an exhaustive report
on all of the important and promising applications related to
HS RS missions. There are several very noteworthy and active
fields that should be addressed in future work, including
target/change detection, time-series analysis, multitemporal
fusion/classification, physical-parameter inversion, imagequality
assessment, and various practical applications (e.g.,
precision farming and disaster management and response).
Moreover, some crucial steps for HS image preprocessing
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE JUNE 2021

IEEE Geoscience and Remote Sensing Magazine - June 2021

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