Hyperspectral image classification using nearest feature line embedding approach

Yang Lang Chang, Jin Nan Liu, Chin Chuan Han, Ying Nong Chen

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


Eigenspace projection methods are widely used for feature extraction from hyperspectral images (HSI) for the classification of land cover. Projection transformation is used to reduce higher dimensional feature vectors to lower dimensional vectors for more accurate classification of land cover types. In this paper, a nearest feature line embedding (NFLE) transformation is proposed for the dimension reduction (DR) of an HSI. The NFL measurement is embedded in the transformation during the discriminant analysis phase, instead of the matching phase. Three factors, including class separability, neighborhood structure preservation, and NFL measurement, are considered simultaneously to determine an effective and discriminating transformation in the eigenspaces for land cover classification. Three state-of-the-art classifiers, the nearest-neighbor, support vector machine, and NFL classifiers, were used to classify the reduced features. The proposed NFLE transformation is compared with different feature extraction approaches and evaluated using two benchmark data sets, the MASTER set at Au-Ku and the AVIRIS set at Northwest Tippecanoe County. The experimental results demonstrate that the NFLE approach is effective for DR in land cover classification in the field of Earth remote sensing.

Original languageEnglish
Article number6472286
Pages (from-to)278-287
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number1
StatePublished - Jan 2014


  • Eigenspace projection
  • Feature extraction
  • Hyperspectral images (HSI)
  • Land cover classification
  • Nearest linear line embedding


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