Effects of band selection on the hyperspectral classification

Charoula Andreou; Vassilia Karathanassi; Gerorgia Diamantopoulou

doi:10.1117/12.2022524

23 September 2013 Effects of band selection on the hyperspectral classification

Charoula Andreou, Vassilia Karathanassi, Gerorgia Diamantopoulou

Proceedings Volume 8870, Imaging Spectrometry XVIII; 88700T (2013) https://doi.org/10.1117/12.2022524
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

Abstract

In the hyperspectral theory, data reduction techniques play an important role in the classification processing as hyperspectral imagery contains an immense amount of data posing many challenging problems such as data storage, computational efficiency, and the curse of dimensionality. Hyperspectral band selection technique is a well-known dimensionality reduction approach which retains the physical meaning of the data. It selects a set of bands from the input hyperspectral dataset which comprises the information needed for subsequent hyperspectral image spectroscopy. The majority of the existing state-of-the-art dimensionality reduction methods set criteria to the spectral information which is derived by the whole wavelength in order to define the optimum spectral subspace. These criteria are not associated with the particular classification task but with the data statistics, such as correlation and entropy values. However, each spectral signature of a particular material has spectral characteristics which contribute to distinguish it from other spectral signatures at specific sequential wavelengths. This paper focuses on investigating the effects of band selection on the classification by exploiting the information of sequential bands. More precisely, it is explored 1) whether classification can be optimized when a different set of initial bands is selected per category; 2) whether there is an optimum subset of sequential bands which lead to more accurate classification results. Experiments comprise application of a well-known classification method, the support vector machine (SVM), on real hyperspectral dataset using all the possible subsets of p sequential bands, where p is equal to the dimensionality of the signal subspace. Evaluation of the classification accuracy leads to remarkable conclusions.

Citation Download Citation

Charoula Andreou, Vassilia Karathanassi, and Gerorgia Diamantopoulou "Effects of band selection on the hyperspectral classification", Proc. SPIE 8870, Imaging Spectrometry XVIII, 88700T (23 September 2013); https://doi.org/10.1117/12.2022524

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
9 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Hyperspectral imaging

Data storage

Image classification

Principal component analysis

Signal to noise ratio

Buildings

Vegetation

Show All Keywords

Keywords/Phrases

Search In:

Publication Years