IEEE Geoscience and Remote Sensing Magazine - December 2017 - 37

Advances in Hyperspectral
Image and Signal Processing
A comprehensive overview of the state of the art

LANDSCAPE-©ISTOCKPHOTO.COM/MAXIMKABB,
BACKGROUND ART-ALL-FREE-DOWNLOAD.COM

PEDRAM GHAMISI, NAOTO YOKOYA, JUN LI, WENZHI LIAO,
SICONG LIU, JAVIER PLAZA, BEHNOOD RASTI, AND ANTONIO PLAZA

R

ecent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information.
They have made a plethora of applications feasible for the
analysis of large areas of the Earth's surface. However, a
significant number of factors-such as the high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light-scattering mechanisms
in the acquisition process, and different atmospheric and
geometric distortions-make such data inherently nonlinear and complex, which poses major challenges for
Digital Object Identifier 10.1109/MGRS.2017.2762087
Date of publication: 27 December 2017

DECEMBER 2017

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

e- xisting methodologies to effectively process and analyze
the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image (HSI) and signal
processing and have become a center of attention for researchers worldwide.
This article offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is
categorized into seven broad topics: classification, spectral unmixing, dimensionality reduction (DR), resolution enhancement, HSI denoising and restoration, change detection (CD),
and fast computing. For each topic, we provide a synopsis of
the state-of-the-art approaches and numerical results for validating and evaluating different methodologies, followed by a
discussion of future challenges and research directions.
	

0274-6638/17©2017IEEE

37



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Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - December 2017

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