Spectral-spatial classification of hyperspectral images with k-means++ partitional clustering

Nikolay L. Kazanskiy; Pavel G. Serafimovich; Evgeniy A. Zimichev

doi:10.1117/12.2180543

25 March 2015 Spectral-spatial classification of hyperspectral images with k-means++ partitional clustering

Nikolay L. Kazanskiy, Pavel G. Serafimovich, Evgeniy A. Zimichev

Proceedings Volume 9533, Optical Technologies for Telecommunications 2014; 95330M (2015) https://doi.org/10.1117/12.2180543
Event: Optical Technologies for Telecommunications 2014, 2014, Kazan, Russian Federation

Abstract

We propose and investigate a complex hyperspectral image classification method with regard to the spatial proximity of pixels. Key feature of the method is that it uses common and relatively simple algorithms to attain high accuracy. The method combines the results of pixel-wise support vector machine classification and a set of contours derived from kmeans++ image clustering. To prevent redundant processing of similar data a principal component analysis is used. The method proposed enables the accuracy and speed of hyperspectral image classification to be enhanced.

Citation Download Citation

Nikolay L. Kazanskiy, Pavel G. Serafimovich, and Evgeniy A. Zimichev "Spectral-spatial classification of hyperspectral images with k-means++ partitional clustering", Proc. SPIE 9533, Optical Technologies for Telecommunications 2014, 95330M (25 March 2015); https://doi.org/10.1117/12.2180543

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available