Prof. Hung-Chao Teng Profile

Prof. Hung-Chao Teng

at Tungnan Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 10 October 2017 Paper

Estimating the number of endmembers in hyperspectral imagery using accumulated convex hull vertex and similarity measure

Kang-Pei Wu, Hong-Chao Teng, Jee-Cheng Wu

Proceedings Volume 10427, 104271T (2017) https://doi.org/10.1117/12.2278406

KEYWORDS: Hyperspectral imaging, Image analysis, Error analysis, Data analysis, Image processing, Data processing

Read Abstract +

The key to successful spectral un-mixing is indicating number of endmembers and their corresponding spectral signatures. Nevertheless, correctly estimate the number of end members without a priori knowledge is a very hard task because pixels in a hyperspectral image are always contain a mixture of the several reflected spectra. Currently, Noise Whitened Harsanyi, Farrand, and Chang (NWHFC) and hyperspectral signal subspace identification by minimum error (HySime) are two well-known methods for estimating the number of endmembers. However, in practice, because NWHFC requires fixing the false-alarm probability and HySime needs estimate noise of each spectral band, these two methods may not only ignore small objects but also can’t identify endmembers. In this paper, assuming endmembers in a hyperspectral image can be modeled by convex geometry. We propose a threestage process to estimate the number of endmembers. At the first stage, principal component (PC) is used to transform original image to low-dimensional components for speeding up algorithm execution. At second stage, successive volume maximization (SVMAX) is used to obtain vertex using convex properties. At the third stage, spectral angle mapper (SAM) is used to compute similarity measures among vertex, and minimum SAM value represents vertex separation. Repeat the second and third stages by increasing transformed component dimensions until reach a predefined criteria. The number of endmembers of the image is the vertex with maximum of the vertex separations Finally, the proposed method is applied to synthetic and real AVIRIS and HYDICE hyperspectral data sets for estimating the number of endmembers. The results demonstrate that the proposed method can be used to estimate more reasonable and precise number of endmembers than the two published methods.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years