Classification of vegetation types using a high-spectral and spatial resolution hyperspectral sensor

Edwin M. Winter

doi:10.1117/12.333632

21 December 1998 Classification of vegetation types using a high-spectral and spatial resolution hyperspectral sensor

Edwin M. Winter

Proceedings Volume 3498, Sensors, Systems, and Next-Generation Satellites II; (1998) https://doi.org/10.1117/12.333632
Event: Remote Sensing, 1998, Barcelona, Spain

Abstract

In October 1997, the TRWIS III sensor was mounted into a small airplane for the purpose of collecting hyperspectral data over a variety of scenes in Ventura County, California, a largely agricultural area about 100 km from Los Angeles. The resulting hyperspectral 384 band data was analyzed using two different approaches. The first was a physically based procedure using the ratios of spectra selected based upon ground truth. Ratios between images in different bands is a way to emphasize the spectral difference and minimize the effect of illumination. The spectral bands selected were in the vicinity of the near infrared 'red edge' chlorophyll feature. The second procedure is an image processing procedure to transpose the image cube using an orthogonal subspace projection of the hyperspectral data cube. In general, a transformation over the full spectral region of the data (from approximately 400 nanometers to 2.45 micrometers) did not give results separating the tree types as well as the physically based ratio method. However, if the spectral region was restricted to 20 to 30 bands in vicinity of the red edge feature, then similar vegetation separation was achieved. In this paper, the analysis using both procedures will be discussed and compared.

Citation Download Citation

Edwin M. Winter "Classification of vegetation types using a high-spectral and spatial resolution hyperspectral sensor", Proc. SPIE 3498, Sensors, Systems, and Next-Generation Satellites II, (21 December 1998); https://doi.org/10.1117/12.333632

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