A Comparison of Forest Classification using Hyperion and AVIRIS Hyperspectral Imagery

Author

Cipar, John ; Cooley, Thomas ; Lockwood, Ronald

Author_Institution

Space Vehicles Directorate, Air Force Res. Lab., Hanscom AFB, MA

fYear

2006

fDate

July 31 2006-Aug. 4 2006

Firstpage

1956

Lastpage

1959

Abstract

We test how well a cluster-based unsupervised classification algorithm separates forest land covers. Our test data, Hyperion and AVIRIS images taken in northern Virginia during autumn, provide two spectrally distinct land covers: pine forests and senescent deciduous forests. We find that the algorithm successfully separates these land covers for AVIRIS data that has been spatially aggregated to simulate 30-m Hyperion GSD. The algorithm does not successfully separate the land covers for the Hyperion data.

Keywords

forestry; geophysical signal processing; image classification; vegetation mapping; AVIRIS hyperspectral imagery; Hyperion GSD; Hyperion hyperspectral imagery; cluster based unsupervised classification algorithm; forest classification; forest land covers; northern Virginia; pine forests; senescent deciduous forests; Aircraft; Classification algorithms; Clustering algorithms; Hyperspectral imaging; Hyperspectral sensors; Laboratories; Signal to noise ratio; Spatial resolution; Testing; Vegetation mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on

Conference_Location

Denver, CO

Print_ISBN

0-7803-9510-7

Type

conf

DOI

10.1109/IGARSS.2006.506

Filename

4241653