585 research outputs found
Bayesian Nonparametric Unmixing of Hyperspectral Images
Hyperspectral imaging is an important tool in remote sensing, allowing for
accurate analysis of vast areas. Due to a low spatial resolution, a pixel of a
hyperspectral image rarely represents a single material, but rather a mixture
of different spectra. HSU aims at estimating the pure spectra present in the
scene of interest, referred to as endmembers, and their fractions in each
pixel, referred to as abundances. Today, many HSU algorithms have been
proposed, based either on a geometrical or statistical model. While most
methods assume that the number of endmembers present in the scene is known,
there is only little work about estimating this number from the observed data.
In this work, we propose a Bayesian nonparametric framework that jointly
estimates the number of endmembers, the endmembers itself, and their
abundances, by making use of the Indian Buffet Process as a prior for the
endmembers. Simulation results and experiments on real data demonstrate the
effectiveness of the proposed algorithm, yielding results comparable with
state-of-the-art methods while being able to reliably infer the number of
endmembers. In scenarios with strong noise, where other algorithms provide only
poor results, the proposed approach tends to overestimate the number of
endmembers slightly. The additional endmembers, however, often simply represent
noisy replicas of present endmembers and could easily be merged in a
post-processing step
Probabilistic Mixture Model-Based Spectral Unmixing
Identifying pure components in mixtures is a common yet challenging problem.
The associated unmixing process requires the pure components, also known as
endmembers, to be sufficiently spectrally distinct. Even with this requirement
met, extracting the endmembers from a single mixture is impossible; an ensemble
of mixtures with sufficient diversity is needed. Several spectral unmixing
approaches have been proposed, many of which are connected to hyperspectral
imaging. However, most of them assume highly diverse collections of mixtures
and extremely low-loss spectroscopic measurements. Additionally, non-Bayesian
frameworks do not incorporate the uncertainty inherent in unmixing. We propose
a probabilistic inference approach that explicitly incorporates noise and
uncertainty, enabling us to unmix endmembers in collections of mixtures with
limited diversity. We use a Bayesian mixture model to jointly extract endmember
spectra and mixing parameters while explicitly modeling observation noise and
the resulting inference uncertainties. We obtain approximate distributions over
endmember coordinates for each set of observed spectra while remaining robust
to inference biases from the lack of pure observations and presence of
non-isotropic Gaussian noise. Access to reliable uncertainties on the unmixing
solutions would enable robust solutions as well as informed decision making
The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch
Recent and forthcoming advances in instrumentation, and giant new surveys,
are creating astronomical data sets that are not amenable to the methods of
analysis familiar to astronomers. Traditional methods are often inadequate not
merely because of the size in bytes of the data sets, but also because of the
complexity of modern data sets. Mathematical limitations of familiar algorithms
and techniques in dealing with such data sets create a critical need for new
paradigms for the representation, analysis and scientific visualization (as
opposed to illustrative visualization) of heterogeneous, multiresolution data
across application domains. Some of the problems presented by the new data sets
have been addressed by other disciplines such as applied mathematics,
statistics and machine learning and have been utilized by other sciences such
as space-based geosciences. Unfortunately, valuable results pertaining to these
problems are mostly to be found only in publications outside of astronomy. Here
we offer brief overviews of a number of concepts, techniques and developments,
some "old" and some new. These are generally unknown to most of the
astronomical community, but are vital to the analysis and visualization of
complex datasets and images. In order for astronomers to take advantage of the
richness and complexity of the new era of data, and to be able to identify,
adopt, and apply new solutions, the astronomical community needs a certain
degree of awareness and understanding of the new concepts. One of the goals of
this paper is to help bridge the gap between applied mathematics, artificial
intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in
Astronomy, special issue "Robotic Astronomy
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
Deep Active Learning for Computer Vision: Past and Future
As an important data selection schema, active learning emerges as the
essential component when iterating an Artificial Intelligence (AI) model. It
becomes even more critical given the dominance of deep neural network based
models, which are composed of a large number of parameters and data hungry, in
application. Despite its indispensable role for developing AI models, research
on active learning is not as intensive as other research directions. In this
paper, we present a review of active learning through deep active learning
approaches from the following perspectives: 1) technical advancements in active
learning, 2) applications of active learning in computer vision, 3) industrial
systems leveraging or with potential to leverage active learning for data
iteration, 4) current limitations and future research directions. We expect
this paper to clarify the significance of active learning in a modern AI model
manufacturing process and to bring additional research attention to active
learning. By addressing data automation challenges and coping with automated
machine learning systems, active learning will facilitate democratization of AI
technologies by boosting model production at scale.Comment: Accepted by APSIPA Transactions on Signal and Information Processin
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