Call for Papers
IEEE Journal on Selected Topics in Signal Processing
Special issue on Advances in Hyperspectral Data Processing and Analysis
Optical sensing has come a long way from gray-scale to multispectral to
hyperspectral images. The advances in imaging hardware over recent
decades have enabled availability of high spatial, spectral and temporal
resolution imagery for a variety of applications. Hyperspectral imagery,
also called imaging spec- troscopy, consists in acquiring images of a
given area using a large number (typically a few hundreds) of narrow and
contiguous spectral bands, covering a wide range of the electromagnetic
spectrum from the vis- ible to the infrared domain. These advances have
created unique challenges for researchers in the remote sensing community
working on algorithms for representation, exploitation and analysis of
such data. At the same time, availability of hyperspectral imaging
capabilities for a wide variety of applications has grown substantially
in the past decade owing primarily to lower hardware costs of imaging
systems operating in the visible, very near-infrared and short-wave
infrared regions of the electromagnetic spectrum.
The emergence of such imagery has, however, created a unique need for
fundamental theory and algo- rithms research to exploit the rich
spectral-spatial-temporal data provided by such imaging sensors. We
invite authors to submit articles representing the cutting edge in signal
and image processing topics related to hyperspectral analysis, including
(but not limited to):
* Advances in classifcation techniques, including feature extraction and
dimensionality reduction (linear/nonlinear, parametric/non-parametric,
supervised / unsupervised / semi-supervised), Bayesian and statistical
signal processing, graph theoretic signal representations, manifold
learning, kernel methods, etc.
* Advances in spectral unmixing (linear/nonlinear, supervised,
unsupervised, and semi-supervised)
* Convex and non-convex optimization, sparsity and `p norm minimization
(including block-sparsity, multiple-measurement models, sparse
regression, dictionary learning, image inpainting, etc.)
* Target and Anomaly Detection (under challenging operational scenario,
such as sub-pixel detection)
* Contextual information based image processing (including Markov random
random elds etc.)
* Data compression for hyperspectral imaging
* Compressive sensing for hyperspectral imaging (optical and signal
processing considerations).
Prospective authors should visit
http://www.signalprocessingsociety.org/publications/periodicals/jstsp/
for information on paper submission. Manuscripts should be submitted at
http://mc.manuscriptcentral.com/jstsp-ieee.
Important Dates:
* Manuscript submission due: September 1, 2014
* First review completed: November 15, 2014
* Revised manuscript due: December 31, 2014
* Second review completed: February 15, 2015
* Final manuscript due: April 1, 2015
* Publication date: September 2015
Guest Editors:
* Saurabh Prasad, University of Houston, USA, sprasad2@uh.edu
* Jocelyn Chanussot, Grenoble Institute of Technology, France,
jocelyn.chanussot@gipsa-lab.grenoble-
* James Fowler, Mississippi State University, USA, fowler@ece.msstate.edu
* Jose M. Bioucas Dias, Instituto de Telecomunicacoes, Portugal,
bioucas@lx.it.pt
* Charles Creusere, New Mexico State University, USA, ccreuser@nmsu.edu
--
※ 發信站: 批踢踢實業坊(ptt.cc)
◆ From: 147.46.241.183