234 research outputs found
Exploiting Structural Complexity for Robust and Rapid Hyperspectral Imaging
This paper presents several strategies for spectral de-noising of
hyperspectral images and hypercube reconstruction from a limited number of
tomographic measurements. In particular we show that the non-noisy spectral
data, when stacked across the spectral dimension, exhibits low-rank. On the
other hand, under the same representation, the spectral noise exhibits a banded
structure. Motivated by this we show that the de-noised spectral data and the
unknown spectral noise and the respective bands can be simultaneously estimated
through the use of a low-rank and simultaneous sparse minimization operation
without prior knowledge of the noisy bands. This result is novel for for
hyperspectral imaging applications. In addition, we show that imaging for the
Computed Tomography Imaging Systems (CTIS) can be improved under limited angle
tomography by using low-rank penalization. For both of these cases we exploit
the recent results in the theory of low-rank matrix completion using nuclear
norm minimization
Sparse multivariate analyses via â„“1-regularized optimization problems solved with Bregman iterative techniques
2012 Fall.Includes bibliographical references.In this dissertation we propose Split Bregman algorithms for several multivariate analytic techniques for dimensionality reduction and feature selection including Sparse Principal Components Analysis, Bisparse Singular Value Decomposition (BSSVD) and Bisparse Singular Value Decomposition with an â„“1-constrained classifier BSSVDâ„“1. For each of these problems we construct and solve a new optimization problem using these Bregman iterative techniques. Each of the proposed optimization problems contain one or more â„“1-regularization terms to enforce sparsity in the solutions. The use of the â„“1-norm to enforce sparsity is a widely used technique, however, its lack of differentiability makes it more difficult to solve problems including these types of terms. Bregman iterations make these solutions possible without the addition of variables and algorithms such as the Split Bregman algorithm makes additional penalty terms and multiple â„“1 terms feasible, a trait that is not present in other state of the art algorithms such as the fixed point continuation algorithm. It is also shown empirically to be faster than another iterative solver for total variation image denoising, another â„“1-regularized problem, in. We also link sparse Principal Components to cluster centers, denoise Hyperspectral Images using the BSSVD, identify and remove ambiguous observations from a classification problem using the algorithm and detect anomalistic subgraphs using Sparse Eigenvectors of the Modularity Matrix
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
Discriminative variable selection for clustering with the sparse Fisher-EM algorithm
The interest in variable selection for clustering has increased recently due
to the growing need in clustering high-dimensional data. Variable selection
allows in particular to ease both the clustering and the interpretation of the
results. Existing approaches have demonstrated the efficiency of variable
selection for clustering but turn out to be either very time consuming or not
sparse enough in high-dimensional spaces. This work proposes to perform a
selection of the discriminative variables by introducing sparsity in the
loading matrix of the Fisher-EM algorithm. This clustering method has been
recently proposed for the simultaneous visualization and clustering of
high-dimensional data. It is based on a latent mixture model which fits the
data into a low-dimensional discriminative subspace. Three different approaches
are proposed in this work to introduce sparsity in the orientation matrix of
the discriminative subspace through -type penalizations. Experimental
comparisons with existing approaches on simulated and real-world data sets
demonstrate the interest of the proposed methodology. An application to the
segmentation of hyperspectral images of the planet Mars is also presented
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
Deep Hyperspectral and Multispectral Image Fusion with Inter-image Variability
Hyperspectral and multispectral image fusion allows us to overcome the
hardware limitations of hyperspectral imaging systems inherent to their lower
spatial resolution. Nevertheless, existing algorithms usually fail to consider
realistic image acquisition conditions. This paper presents a general imaging
model that considers inter-image variability of data from heterogeneous sources
and flexible image priors. The fusion problem is stated as an optimization
problem in the maximum a posteriori framework. We introduce an original image
fusion method that, on the one hand, solves the optimization problem accounting
for inter-image variability with an iteratively reweighted scheme and, on the
other hand, that leverages light-weight CNN-based networks to learn realistic
image priors from data. In addition, we propose a zero-shot strategy to
directly learn the image-specific prior of the latent images in an unsupervised
manner. The performance of the algorithm is illustrated with real data subject
to inter-image variability.Comment: IEEE Trans. Geosci. Remote sens., to be published. Manuscript
submitted August 23, 2022; revised Dec. 15, 2022, and Mar. 13, 2023; and
accepted Apr. 07, 202
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