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A Modified Nonparametric Weight Feature Extraction Using Spatial and Spectral Information

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4 Author(s)
Bor-Chen Kuo ; Grad. Sch. of Educ. Meas. & Stat., Nat. Taichung Univ., Taichung ; Chih-Cheng Hung ; Chen-Wei Chang ; Hsuan-Po Wang

Feature extraction is often applied for dimensionality reduction in hyperspectral data classification problems to mitigate the Hughes phenomenon. Some studies had proven that nonparametric weighted feature extraction (NWFE) is a powerful tool to extract well-described features for classification. NWFE concentrates only on the separability of spectral data, however, in many remotely sensed images, objects on the ground are much greater than one pixel. Hence, neighboring pixels are more likely to belong to the same class and form a homogeneous region. We present a scheme to fuse spatial information into NWFE, and from the real data experiments, we can find the proposed method outperforms the original NWFE.

Published in:

Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on

Date of Conference:

July 31 2006-Aug. 4 2006