Share Email Print

Proceedings Paper

Deep canonical correlation analysis for hyperspectral image classification
Author(s): Kemal Gürkan Toker; Seniha Esen Yüksel
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Multi-view learning (MVL) is a technique which utilizes multiple views of data simultaneously during training to learn more expressive representations. Multi-view learning has been gaining a large amount of interest in various machine learning applications recently. In this paper, we focus on learning representations prior to classification using multi-view learning via deep canonical correlation analysis (DCCA) in hyperspectral image processing. We propose a classification framework including a proposed view generation approach. The motivation of our proposed view generation approach is to fuse spatial and spectral information. The performance of our proposed view generation approach is compared with the other view generation methods in the literature; namely the uniform band slicing and correlation-partition-based clustering. To evaluate the effectiveness of the proposed approach, we performed experiments on two commonly used hyperspectral image datasets. Experimental results based on two hyperspectral image datasets demonstrate that the proposed classification framework provides satisfactory classification performances.

Paper Details

Date Published: 14 October 2019
PDF: 7 pages
Proc. SPIE 11150, Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2019, 1115009 (14 October 2019); doi: 10.1117/12.2532467
Show Author Affiliations
Kemal Gürkan Toker, Hacettepe Univ. (Turkey)
Aselsan Inc. (Turkey)
Seniha Esen Yüksel, Hacettepe Univ. (Turkey)

Published in SPIE Proceedings Vol. 11150:
Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 2019
Charles R. Bostater Jr.; Xavier Neyt; Françoise Viallefont-Robinet, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?