TY - JOUR

T1 - A post-processing technique for Lagrangian artificial neural network approach to hyperspectral image classification

AU - Du, Qian

AU - Szu, Harold

AU - Ren, Hsuan

PY - 2003

Y1 - 2003

N2 - 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 corresponding 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 ns 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 ns estimated in the first stage. The experimental results using AVIRIS data shows the efficiency of the proposed post-processing technique in distinct AFI selection.

AB - 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 corresponding 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 ns 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 ns estimated in the first stage. The experimental results using AVIRIS data shows the efficiency of the proposed post-processing technique in distinct AFI selection.

KW - Blind source separation

KW - Classification

KW - Hyperspectral imaging

KW - Independent component analysis

KW - Lagrangian artificial neural network

UR - http://www.scopus.com/inward/record.url?scp=0042326407&partnerID=8YFLogxK

U2 - 10.1117/12.485964

DO - 10.1117/12.485964

M3 - 會議論文

AN - SCOPUS:0042326407

SN - 0277-786X

VL - 5102

SP - 17

EP - 24

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Independent Component Analyses, Wavelets, And Neural Networks

Y2 - 22 April 2003 through 25 April 2003

ER -