53,927 research outputs found
Locating Multiple Multi-scale Electromagnetic Scatterers by A Single Far-field Measurement
Two inverse scattering schemes were recently developed in
\cite{LiLiuShangSun} for locating multiple electromagnetic (EM) scatterers,
respectively, of small size and regular size compared to the detecting EM
wavelength. Both schemes make use of a single far-field measurement. The scheme
of locating regular-size scatterers requires the {\it a priori} knowledge of
the possible shapes, orientations and sizes of the underlying scatterer
components. In this paper, we extend that imaging scheme to a much more
practical setting by relaxing the requirement on the orientations and sizes. We
also develop an imaging scheme of locating multiple multi-scale EM scatterers,
which may include at the same time, both components of regular size and small
size. For the second scheme, a novel local re-sampling technique is developed.
Furthermore, more robust and accurate reconstruction can be achieved for the
second scheme if an additional far-field measurement is used. Rigorous
mathematical justifications are provided and numerical results are presented to
demonstrate the effectiveness and the promising features of the proposed
imaging schemes.Comment: Any comments are welcom
Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification
Hyperspectral image (HSI) classification, which aims to assign an accurate
label for hyperspectral pixels, has drawn great interest in recent years.
Although low rank representation (LRR) has been used to classify HSI, its
ability to segment each class from the whole HSI data has not been exploited
fully yet. LRR has a good capacity to capture the underlying lowdimensional
subspaces embedded in original data. However, there are still two drawbacks for
LRR. First, LRR does not consider the local geometric structure within data,
which makes the local correlation among neighboring data easily ignored.
Second, the representation obtained by solving LRR is not discriminative enough
to separate different data. In this paper, a novel locality and structure
regularized low rank representation (LSLRR) model is proposed for HSI
classification. To overcome the above limitations, we present locality
constraint criterion (LCC) and structure preserving strategy (SPS) to improve
the classical LRR. Specifically, we introduce a new distance metric, which
combines both spatial and spectral features, to explore the local similarity of
pixels. Thus, the global and local structures of HSI data can be exploited
sufficiently. Besides, we propose a structure constraint to make the
representation have a near block-diagonal structure. This helps to determine
the final classification labels directly. Extensive experiments have been
conducted on three popular HSI datasets. And the experimental results
demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.Comment: 14 pages, 7 figures, TGRS201
Optimal Clustering Framework for Hyperspectral Band Selection
Band selection, by choosing a set of representative bands in hyperspectral
image (HSI), is an effective method to reduce the redundant information without
compromising the original contents. Recently, various unsupervised band
selection methods have been proposed, but most of them are based on
approximation algorithms which can only obtain suboptimal solutions toward a
specific objective function. This paper focuses on clustering-based band
selection, and proposes a new framework to solve the above dilemma, claiming
the following contributions: 1) An optimal clustering framework (OCF), which
can obtain the optimal clustering result for a particular form of objective
function under a reasonable constraint. 2) A rank on clusters strategy (RCS),
which provides an effective criterion to select bands on existing clustering
structure. 3) An automatic method to determine the number of the required
bands, which can better evaluate the distinctive information produced by
certain number of bands. In experiments, the proposed algorithm is compared to
some state-of-the-art competitors. According to the experimental results, the
proposed algorithm is robust and significantly outperform the other methods on
various data sets
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