1,263 research outputs found
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
Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification
Despite the fact that nonlinear subspace learning techniques (e.g. manifold
learning) have successfully applied to data representation, there is still room
for improvement in explainability (explicit mapping), generalization
(out-of-samples), and cost-effectiveness (linearization). To this end, a novel
linearized subspace learning technique is developed in a joint and progressive
way, called \textbf{j}oint and \textbf{p}rogressive \textbf{l}earning
str\textbf{a}teg\textbf{y} (J-Play), with its application to multi-label
classification. The J-Play learns high-level and semantically meaningful
feature representation from high-dimensional data by 1) jointly performing
multiple subspace learning and classification to find a latent subspace where
samples are expected to be better classified; 2) progressively learning
multi-coupled projections to linearly approach the optimal mapping bridging the
original space with the most discriminative subspace; 3) locally embedding
manifold structure in each learnable latent subspace. Extensive experiments are
performed to demonstrate the superiority and effectiveness of the proposed
method in comparison with previous state-of-the-art methods.Comment: accepted in ECCV 201
Spectral Unmixing with Multiple Dictionaries
Spectral unmixing aims at recovering the spectral signatures of materials,
called endmembers, mixed in a hyperspectral or multispectral image, along with
their abundances. A typical assumption is that the image contains one pure
pixel per endmember, in which case spectral unmixing reduces to identifying
these pixels. Many fully automated methods have been proposed in recent years,
but little work has been done to allow users to select areas where pure pixels
are present manually or using a segmentation algorithm. Additionally, in a
non-blind approach, several spectral libraries may be available rather than a
single one, with a fixed number (or an upper or lower bound) of endmembers to
chose from each. In this paper, we propose a multiple-dictionary constrained
low-rank matrix approximation model that address these two problems. We propose
an algorithm to compute this model, dubbed M2PALS, and its performance is
discussed on both synthetic and real hyperspectral images
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
Investigation of feature extraction algorithms and techniques for hyperspectral images.
Doctor of Philosophy (Computer Engineering). University of KwaZulu-Natal. Durban, 2017.Hyperspectral images (HSIs) are remote-sensed images that are characterized
by very high spatial and spectral dimensions and nd applications, for example,
in land cover classi cation, urban planning and management, security and food
processing. Unlike conventional three bands RGB images, their high
dimensional data space creates a challenge for traditional image processing
techniques which are usually based on the assumption that there exists
su cient training samples in order to increase the likelihood of high
classi cation accuracy. However, the high cost and di culty of obtaining
ground truth of hyperspectral data sets makes this assumption unrealistic and
necessitates the introduction of alternative methods for their processing.
Several techniques have been developed in the exploration of the rich spectral
and spatial information in HSIs. Speci cally, feature extraction (FE)
techniques are introduced in the processing of HSIs as a necessary step before
classi cation. They are aimed at transforming the high dimensional data of the
HSI into one of a lower dimension while retaining as much spatial and/or
spectral information as possible. In this research, we develop semi-supervised
FE techniques which combine features of supervised and unsupervised
techniques into a single framework for the processing of HSIs. Firstly, we
developed a feature extraction algorithm known as Semi-Supervised Linear
Embedding (SSLE) for the extraction of features in HSI. The algorithm
combines supervised Linear Discriminant Analysis (LDA) and unsupervised
Local Linear Embedding (LLE) to enhance class discrimination while also
preserving the properties of classes of interest. The technique was developed
based on the fact that LDA extracts features from HSIs by discriminating
between classes of interest and it can only extract C 1 features provided there
are C classes in the image by extracting features that are equivalent to the
number of classes in the HSI. Experiments show that the SSLE algorithm
overcomes the limitation of LDA and extracts features that are equivalent to
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the number of classes in HSIs. Secondly, a graphical manifold dimension
reduction (DR) algorithm known as Graph Clustered Discriminant Analysis
(GCDA) is developed. The algorithm is developed to dynamically select labeled
samples from the pool of available unlabeled samples in order to complement
the few available label samples in HSIs. The selection is achieved by entwining
K-means clustering with a semi-supervised manifold discriminant analysis.
Using two HSI data sets, experimental results show that GCDA extracts
features that are equivalent to the number of classes with high classi cation
accuracy when compared with other state-of-the-art techniques. Furthermore,
we develop a window-based partitioning approach to preserve the spatial
properties of HSIs when their features are being extracted. In this approach,
the HSI is partitioned along its spatial dimension into n windows and the
covariance matrices of each window are computed. The covariance matrices of
the windows are then merged into a single matrix through using the Kalman
ltering approach so that the resulting covariance matrix may be used for
dimension reduction. Experiments show that the windowing approach achieves
high classi cation accuracy and preserves the spatial properties of HSIs. For
the proposed feature extraction techniques, Support Vector Machine (SVM)
and Neural Networks (NN) classi cation techniques are employed and their
performances are compared for these two classi ers. The performances of all
proposed FE techniques have also been shown to outperform other
state-of-the-art approaches
Semi-supervised linear spectral unmixing using a hierarchical Bayesian model for hyperspectral imagery
This paper proposes a hierarchical Bayesian model that can be used for semi-supervised hyperspectral image unmixing. The model assumes that the pixel reflectances result from linear combinations of pure component spectra contaminated by an additive Gaussian noise. The abundance parameters appearing in this model satisfy positivity and additivity constraints. These constraints are naturally expressed in a Bayesian context by using appropriate abundance prior distributions. The posterior distributions of the unknown model parameters are then derived. A Gibbs sampler allows one to draw samples distributed according to the posteriors of interest and to estimate the unknown abundances. An extension of the algorithm is finally studied for mixtures with unknown numbers of spectral components belonging to a know library. The performance of the different unmixing strategies is evaluated via simulations conducted on synthetic and real data
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