Feature selection technique for classification of hyperspectral AVIRIS data

Sylvia S. Shen; Bonnie Y. Trang

doi:10.1117/12.50814

1 December 1991 Feature selection technique for classification of hyperspectral AVIRIS data

Sylvia S. Shen, Bonnie Y. Trang

Proceedings Volume 1567, Applications of Digital Image Processing XIV; (1991) https://doi.org/10.1117/12.50814
Event: San Diego, '91, 1991, San Diego, CA, United States

Abstract

A new generation of remote sensing instruments, called imaging spectrometers, are designed to collect image data in literally hundreds of spectral channels simultaneously, providing significantly enhanced amounts of information as compared to existing systems for studying biophysical processes. The advantage of such high-dimensional data comes at the cost of increased data complexity. The volume and complexity of the new data, in turn, presents a challenge to the traditional image analysis methods and requires that new approaches be developed to allow rapid and effective analysis of the imagery. This paper describes a technique which reduces the data dimensionality, while retaining sufficient pertinent information that the original high-dimensional signals provide for class separation. Results of applying this technique to 224-band AVIRIS data are presented.

Citation Download Citation

Sylvia S. Shen and Bonnie Y. Trang "Feature selection technique for classification of hyperspectral AVIRIS data", Proc. SPIE 1567, Applications of Digital Image Processing XIV, (1 December 1991); https://doi.org/10.1117/12.50814

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