3,161 research outputs found
Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization
Incorporating spatial information into hyperspectral unmixing procedures has
been shown to have positive effects, due to the inherent spatial-spectral
duality in hyperspectral scenes. Current research works that consider spatial
information are mainly focused on the linear mixing model. In this paper, we
investigate a variational approach to incorporating spatial correlation into a
nonlinear unmixing procedure. A nonlinear algorithm operating in reproducing
kernel Hilbert spaces, associated with an local variation norm as the
spatial regularizer, is derived. Experimental results, with both synthetic and
real data, illustrate the effectiveness of the proposed scheme.Comment: 5 pages, 1 figure, submitted to ICASSP 201
Discriminative Features via Generalized Eigenvectors
Representing examples in a way that is compatible with the underlying
classifier can greatly enhance the performance of a learning system. In this
paper we investigate scalable techniques for inducing discriminative features
by taking advantage of simple second order structure in the data. We focus on
multiclass classification and show that features extracted from the generalized
eigenvectors of the class conditional second moments lead to classifiers with
excellent empirical performance. Moreover, these features have attractive
theoretical properties, such as inducing representations that are invariant to
linear transformations of the input. We evaluate classifiers built from these
features on three different tasks, obtaining state of the art results
GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection
Change detection (CD) is an important application of remote sensing, which
provides timely change information about large-scale Earth surface. With the
emergence of hyperspectral imagery, CD technology has been greatly promoted, as
hyperspectral data with the highspectral resolution are capable of detecting
finer changes than using the traditional multispectral imagery. Nevertheless,
the high dimension of hyperspectral data makes it difficult to implement
traditional CD algorithms. Besides, endmember abundance information at subpixel
level is often not fully utilized. In order to better handle high dimension
problem and explore abundance information, this paper presents a General
End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image
change detection (HSI-CD). The main contributions of this work are threefold:
1) Mixed-affinity matrix that integrates subpixel representation is introduced
to mine more cross-channel gradient features and fuse multi-source information;
2) 2-D CNN is designed to learn the discriminative features effectively from
multi-source data at a higher level and enhance the generalization ability of
the proposed CD algorithm; 3) A new HSI-CD data set is designed for the
objective comparison of different methods. Experimental results on real
hyperspectral data sets demonstrate the proposed method outperforms most of the
state-of-the-arts
Robust PCA as Bilinear Decomposition with Outlier-Sparsity Regularization
Principal component analysis (PCA) is widely used for dimensionality
reduction, with well-documented merits in various applications involving
high-dimensional data, including computer vision, preference measurement, and
bioinformatics. In this context, the fresh look advocated here permeates
benefits from variable selection and compressive sampling, to robustify PCA
against outliers. A least-trimmed squares estimator of a low-rank bilinear
factor analysis model is shown closely related to that obtained from an
-(pseudo)norm-regularized criterion encouraging sparsity in a matrix
explicitly modeling the outliers. This connection suggests robust PCA schemes
based on convex relaxation, which lead naturally to a family of robust
estimators encompassing Huber's optimal M-class as a special case. Outliers are
identified by tuning a regularization parameter, which amounts to controlling
sparsity of the outlier matrix along the whole robustification path of (group)
least-absolute shrinkage and selection operator (Lasso) solutions. Beyond its
neat ties to robust statistics, the developed outlier-aware PCA framework is
versatile to accommodate novel and scalable algorithms to: i) track the
low-rank signal subspace robustly, as new data are acquired in real time; and
ii) determine principal components robustly in (possibly) infinite-dimensional
feature spaces. Synthetic and real data tests corroborate the effectiveness of
the proposed robust PCA schemes, when used to identify aberrant responses in
personality assessment surveys, as well as unveil communities in social
networks, and intruders from video surveillance data.Comment: 30 pages, submitted to IEEE Transactions on Signal Processin
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