Assessing band selection and image classification techniques on HYDICE hyperspectral data

Author

Marin, John A. ; Brockhaus, John ; Rolf, James ; Shine, James ; Schafer, Joseph ; Balthazor, Andrew

Author_Institution

Dept. of Electr. Eng. & Comput. Sci., US Mil. Acad., West Point, NY, USA

Volume

1

fYear

1999

fDate

1999

Firstpage

1067

Abstract

Describes some preliminary results concerning the robustness and generalization capabilities of manual (parametric) methods versus machine learning methods in the band selection and subsequent classification of hyperspectral images. We specifically compare a genetic algorithm-based approach to an end-member and spectral unmixing approach in the selection of data used for classification of a HYDICE image. We then discuss and compare the classification of the image using supervised and unsupervised techniques, as well as a backpropagation neural network

Keywords

backpropagation; generalisation (artificial intelligence); image classification; neural nets; HYDICE hyperspectral data; HYDICE image; backpropagation neural network; band selection; generalization capabilities; genetic algorithm-based approach; image classification techniques; machine learning methods; manual methods; parametric methods; robustness; spectral unmixing approach; supervised techniques; unsupervised techniques; Genetic algorithms; Hyperspectral imaging; Image classification; Lakes; Learning systems; Military computing; Neural networks; Pixel; Roads; Robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on

Conference_Location

Tokyo

ISSN

1062-922X

Print_ISBN

0-7803-5731-0

Type

conf

DOI

10.1109/ICSMC.1999.814241

Filename

814241