207 research outputs found
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Hyperspectral imaging, also known as image spectrometry, is a landmark
technique in geoscience and remote sensing (RS). In the past decade, enormous
efforts have been made to process and analyze these hyperspectral (HS) products
mainly by means of seasoned experts. However, with the ever-growing volume of
data, the bulk of costs in manpower and material resources poses new challenges
on reducing the burden of manual labor and improving efficiency. For this
reason, it is, therefore, urgent to develop more intelligent and automatic
approaches for various HS RS applications. Machine learning (ML) tools with
convex optimization have successfully undertaken the tasks of numerous
artificial intelligence (AI)-related applications. However, their ability in
handling complex practical problems remains limited, particularly for HS data,
due to the effects of various spectral variabilities in the process of HS
imaging and the complexity and redundancy of higher dimensional HS signals.
Compared to the convex models, non-convex modeling, which is capable of
characterizing more complex real scenes and providing the model
interpretability technically and theoretically, has been proven to be a
feasible solution to reduce the gap between challenging HS vision tasks and
currently advanced intelligent data processing models
Image Restoration for Remote Sensing: Overview and Toolbox
Remote sensing provides valuable information about objects or areas from a
distance in either active (e.g., RADAR and LiDAR) or passive (e.g.,
multispectral and hyperspectral) modes. The quality of data acquired by
remotely sensed imaging sensors (both active and passive) is often degraded by
a variety of noise types and artifacts. Image restoration, which is a vibrant
field of research in the remote sensing community, is the task of recovering
the true unknown image from the degraded observed image. Each imaging sensor
induces unique noise types and artifacts into the observed image. This fact has
led to the expansion of restoration techniques in different paths according to
each sensor type. This review paper brings together the advances of image
restoration techniques with particular focuses on synthetic aperture radar and
hyperspectral images as the most active sub-fields of image restoration in the
remote sensing community. We, therefore, provide a comprehensive,
discipline-specific starting point for researchers at different levels (i.e.,
students, researchers, and senior researchers) willing to investigate the
vibrant topic of data restoration by supplying sufficient detail and
references. Additionally, this review paper accompanies a toolbox to provide a
platform to encourage interested students and researchers in the field to
further explore the restoration techniques and fast-forward the community. The
toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS
A Constrained Convex Optimization Approach to Hyperspectral Image Restoration with Hybrid Spatio-Spectral Regularization
We propose a new constrained optimization approach to hyperspectral (HS)
image restoration. Most existing methods restore a desirable HS image by
solving some optimization problem, which consists of a regularization term(s)
and a data-fidelity term(s). The methods have to handle a regularization
term(s) and a data-fidelity term(s) simultaneously in one objective function,
and so we need to carefully control the hyperparameter(s) that balances these
terms. However, the setting of such hyperparameters is often a troublesome task
because their suitable values depend strongly on the regularization terms
adopted and the noise intensities on a given observation. Our proposed method
is formulated as a convex optimization problem, where we utilize a novel hybrid
regularization technique named Hybrid Spatio-Spectral Total Variation (HSSTV)
and incorporate data-fidelity as hard constraints. HSSTV has a strong ability
of noise and artifact removal while avoiding oversmoothing and spectral
distortion, without combining other regularizations such as low-rank
modeling-based ones. In addition, the constraint-type data-fidelity enables us
to translate the hyperparameters that balance between regularization and
data-fidelity to the upper bounds of the degree of data-fidelity that can be
set in a much easier manner. We also develop an efficient algorithm based on
the alternating direction method of multipliers (ADMM) to efficiently solve the
optimization problem. Through comprehensive experiments, we illustrate the
advantages of the proposed method over various HS image restoration methods
including state-of-the-art ones.Comment: 20 pages, 4 tables, 10 figures, submitted to MDPI Remote Sensin
A General Destriping Framework for Remote Sensing Images Using Flatness Constraint
This paper proposes a general destriping framework using flatness
constraints, where we can handle various regularization functions in a unified
manner. Removing stripe noise, i.e., destriping, from remote sensing images is
an essential task in terms of visual quality and subsequent processing. Most of
the existing methods are designed by combining a particular image
regularization with a stripe noise characterization that cooperates with the
regularization, which precludes us to examine different regularizations to
adapt to various target images. To resolve this, we formulate the destriping
problem as a convex optimization problem involving a general form of image
regularization and the flatness constraints, a newly introduced stripe noise
characterization. This strong characterization enables us to consistently
capture the nature of stripe noise, regardless of the choice of image
regularization. For solving the optimization problem, we also develop an
efficient algorithm based on a diagonally preconditioned primal-dual splitting
algorithm (DP-PDS), which can automatically adjust the stepsizes. The
effectiveness of our framework is demonstrated through destriping experiments,
where we comprehensively compare combinations of image regularizations and
stripe noise characterizations using hyperspectral images (HSI) and infrared
(IR) videos.Comment: submitted to IEEE Transactions on Geoscience and Remote Sensin
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Imaging spectrometers measure electromagnetic energy scattered in their
instantaneous field view in hundreds or thousands of spectral channels with
higher spectral resolution than multispectral cameras. Imaging spectrometers
are therefore often referred to as hyperspectral cameras (HSCs). Higher
spectral resolution enables material identification via spectroscopic analysis,
which facilitates countless applications that require identifying materials in
scenarios unsuitable for classical spectroscopic analysis. Due to low spatial
resolution of HSCs, microscopic material mixing, and multiple scattering,
spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus,
accurate estimation requires unmixing. Pixels are assumed to be mixtures of a
few materials, called endmembers. Unmixing involves estimating all or some of:
the number of endmembers, their spectral signatures, and their abundances at
each pixel. Unmixing is a challenging, ill-posed inverse problem because of
model inaccuracies, observation noise, environmental conditions, endmember
variability, and data set size. Researchers have devised and investigated many
models searching for robust, stable, tractable, and accurate unmixing
algorithms. This paper presents an overview of unmixing methods from the time
of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models
are first discussed. Signal-subspace, geometrical, statistical, sparsity-based,
and spatial-contextual unmixing algorithms are described. Mathematical problems
and potential solutions are described. Algorithm characteristics are
illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensin
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Spectral pixels are often a mixture of the pure spectra of the materials,
called endmembers, due to the low spatial resolution of hyperspectral sensors,
double scattering, and intimate mixtures of materials in the scenes. Unmixing
estimates the fractional abundances of the endmembers within the pixel.
Depending on the prior knowledge of endmembers, linear unmixing can be divided
into three main groups: supervised, semi-supervised, and unsupervised (blind)
linear unmixing. Advances in Image processing and machine learning
substantially affected unmixing. This paper provides an overview of advanced
and conventional unmixing approaches. Additionally, we draw a critical
comparison between advanced and conventional techniques from the three
categories. We compare the performance of the unmixing techniques on three
simulated and two real datasets. The experimental results reveal the advantages
of different unmixing categories for different unmixing scenarios. Moreover, we
provide an open-source Python-based package available at
https://github.com/BehnoodRasti/HySUPP to reproduce the results
Robust Hyperspectral Image Fusion with Simultaneous Guide Image Denoising via Constrained Convex Optimization
The paper proposes a new high spatial resolution hyperspectral (HR-HS) image
estimation method based on convex optimization. The method assumes a low
spatial resolution HS (LR-HS) image and a guide image as observations, where
both observations are contaminated by noise. Our method simultaneously
estimates an HR-HS image and a noiseless guide image, so the method can utilize
spatial information in a guide image even if it is contaminated by heavy noise.
The proposed estimation problem adopts hybrid spatio-spectral total variation
as regularization and evaluates the edge similarity between HR-HS and guide
images to effectively use apriori knowledge on an HR-HS image and spatial
detail information in a guide image. To efficiently solve the problem, we apply
a primal-dual splitting method. Experiments demonstrate the performance of our
method and the advantage over several existing methods.Comment: Accepted to IEEE Transactions on Geoscience and Remote Sensin
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