Postprocessing technique for Lagrangian artificial neural network approach to hyperspectral image classification

Qian Du; Harold H. Szu; Hsuan Ren

doi:10.1117/12.485964

1 April 2003 Postprocessing technique for Lagrangian artificial neural network approach to hyperspectral image classification

Qian Du, Harold H. Szu, Hsuan Ren

Author Affiliations +

Proceedings Volume 5102, Independent Component Analyses, Wavelets, and Neural Networks; (2003) https://doi.org/10.1117/12.485964
Event: AeroSense 2003, 2003, Orlando, Florida, United States

Abstract

Lagrangian Artificial Neural Network (LANN) has been proposed recently for hyperspectral image classification. It is an unsupervised technique that can simultaneously estimate the endmembers and their abundance fractions without any prior information. Since the implementation of the LANN is completely unsupervised, the number of estimated abundance fraction images (AFI) is equal to the number of bands, which display the distribution of the corresonding endmember materials in the image scene. We find out that many AFIs are highly correlated and visually similar. In order to facilitate the following data assessment, a two-stage post-processing approach will be proposed. First, the number of endmembers n_s resident in the image scene is estimated using a Neyman-Pearson hypothesis testing-based eigen-thresholding method. Next, an automatic searching algorithm will be applied to find the most distinct AFIs using the divergence as criterion, where the threshold is adjusted until the number of selected AFIs equals the n_s estimated in the first stage. The experimental results using AVIRIS data shows the efficiency of the proposed post-processing technique in distinct AFI selection.

Citation Download Citation

Qian Du, Harold H. Szu, and Hsuan Ren "Postprocessing technique for Lagrangian artificial neural network approach to hyperspectral image classification", Proc. SPIE 5102, Independent Component Analyses, Wavelets, and Neural Networks, (1 April 2003); https://doi.org/10.1117/12.485964

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