Introducing training and parameter tuning for KOSP classification of hyperspectral images

L. Capobianco; L. Carli; A. Garzelli; F. Nencini

doi:10.1117/12.738494

29 October 2007 Introducing training and parameter tuning for KOSP classification of hyperspectral images

L. Capobianco, L. Carli, A. Garzelli, F. Nencini

Proceedings Volume 6748, Image and Signal Processing for Remote Sensing XIII; 67480B (2007) https://doi.org/10.1117/12.738494
Event: SPIE Remote Sensing, 2007, Florence, Italy

Abstract

Kernel-based Orthogonal Subspace Projection (KOSP) provides good results in the field of classification of hyperspectral images. However, an open-problem is the evaluation from the ground-truth samples of the prototypes that best represent the classes. In the original formulation of KOSP, this preliminary (training) stage is very simple since for each class the prototype is computed as the centroid of the ground-truth samples. In order to improve KOSP performances, in this paper we introduce a minimization problem to evaluate the best prototypes from a given ground truth of a specific classification problem. K-fold cross-validation is used to avoid overfitting. The performance of the proposed methodology is tested by classifying the widely used 'Indian Pine' hyperspectral dataset collected by the AVIRIS spectrometer.

Citation Download Citation

L. Capobianco, L. Carli, A. Garzelli, and F. Nencini "Introducing training and parameter tuning for KOSP classification of hyperspectral images", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480B (29 October 2007); https://doi.org/10.1117/12.738494

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