Three-Dimensional Surface Feature for Hyperspectral Imagery Classification View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-10-24

AUTHORS

Sen Jia , Kuilin Wu , Meng Zhang , Jie Hu

ABSTRACT

Gabor surface feature (GSF) uses the first order and second order derivatives of Gabor magnitude pictures (GMPs) to jointly represent image. However, GSF can not excavate the contextual information that hides in the spectral-spatial structure of three-dimensional hyperspectral imagery since GSF can only deal with spatial relationships. Meanwhile, GSF runs on GMPs with multi-scale and multi-orientation, which leads to dimensional explosion problem. Aiming at these two problems, three-dimensional surface feature (3DSF) approach is proposed for hyperspectral imagery in this paper. 3DSF directly deals with the raw hyperspectral imagery data and utilizes its first order derivative magnitude to jointly represent hyperspectral imagery. Experiments on three real hyperspectral datasets, including Pavia University, Houston University and Indian Pines, verify the effectiveness of the proposed 3DSF approach. More... »

PAGES

270-278

Book

TITLE

Neural Information Processing

ISBN

978-3-319-70086-1
978-3-319-70087-8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-70087-8_29

DOI

http://dx.doi.org/10.1007/978-3-319-70087-8_29

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092370225


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