@inproceedings{33629b058b7b4614a48e8bb4070b9238,
title = "Nearest feature line embedding approach to hyperspectral image classification",
abstract = "In this paper, a nearest feature line (NFL) embedding transformation is proposed for dimension reduction of hyperspectral image (HSI). Eigenspace projection approaches are generally used for feature extraction of HSI in remote sensing image classification. In order to improve the classification accuracy, the feature vectors of high dimensions are reduced to the low dimensionalities by the effective projection transformation. Similarly, the proposed NFL measurement is embedded into the transformation during the discriminant analysis stage instead of the matching stage. The class separability, neighborhood structure preservation, and NFL measurement are also simultaneously considered to find the effective and discriminating transformation in eigenspaces for image classification. The nearest neighbor classifier is used to show the discriminative performance. The proposed NFL embedding transformation is compared with several conventional state-of-the-art algorithms. It was evaluated by the AVIRIS data sets of Northwest Tippecanoe County. Experimental results have demonstrated that NFL embedding method is an effective transformation for dimension reduction in land cover classification of earth remote sensing.",
keywords = "Hyperspectral images, Land cover classification, Nearest linear line embedding",
author = "Chang, {Yang Lang} and Liu, {Jin Nan} and Han, {Chin Chuan} and Chen, {Ying Nong} and Hsieh, {Tung Ju} and Bormin Huang",
year = "2012",
doi = "10.1117/12.940740",
language = "???core.languages.en_GB???",
isbn = "9780819492319",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Satellite Data Compression, Communications, and Processing VIII",
note = "Satellite Data Compression, Communications, and Processing VIII ; Conference date: 12-08-2012 Through 13-08-2012",
}