Share Email Print
cover

Proceedings Paper

In-track multi-angle model portability of multispectral land-cover classification using very high spatial resolution data
Author(s): Nathan Longbotham; Fabio Pacifici; William Emery
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

In this paper, we present a method to analyze the impact of data space normalization on spectral classification model portability using multi-angle very-high spatial resolution imagery. In-track multi-angle data provide images of a single scene, from different observation angles, during a very short period of time. This creates a sequence of images with relatively static atmospheric and illumination conditions. With this data, the only changes in the scene are due to observation angle and surface reflectance properties. Using this information, we present an analysis of both the impact of surface anisotropy and data space normalization on spectral classification accuracy and model portability.

Paper Details

Date Published: 24 May 2012
PDF: 7 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 83901V (24 May 2012); doi: 10.1117/12.919581
Show Author Affiliations
Nathan Longbotham, Univ. of Colorado at Boulder (United States)
Fabio Pacifici, DigitalGlobe, Inc. (United States)
William Emery, Univ. of Colorado at Boulder (United States)


Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

© SPIE. Terms of Use
Back to Top