Hyperspectral Image Classification Using Principal Components-Based Smooth Ordering and Multiple 1-D Interpolation

Zhijing Ye, Hong Li*, Yalong Song, Jon Atli Benediktsson, Yuan Yan Tang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

This paper proposes a spectral-spatial classification algorithm based on principal components (PCs)-based smooth ordering and multiple 1-D interpolation, which can alleviate the general classification problems effectively. Because of the characteristics of hyperspectral image, there always exist easily separable samples (ESSs) and difficultly separable samples (DSSs) in view of the different sets of labeled samples. In this paper, the PC analysis is first used for reducing features and extracting the few first PCs of a hyperspectral image. Then, PC-based smooth ordering is designed for the separation of ESSs and DSSs, and multiple 1-D interpolation is used for the accurate classification of the ESSs. Next, the highly confident samples are selected from the ESSs by the spatial neighborhood information, which are added into the training set for the classification of DSSs. In the case of sufficient training samples, a supervised spectral-spatial method is used for classifying the DSSs by combining the spatial information built with popular extended multiattribute profiles. The proposed algorithm is compared with some state-of-the-art methods on three hyperspectral data sets. The results demonstrate that the presented algorithm achieves much better classification performance in terms of the accuracy and the computation time.

Original languageEnglish
Article number7748471
Pages (from-to)1199-1209
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume55
Issue number2
DOIs
Publication statusPublished - Feb 2017

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61472155, Grant 91330118, and Grant 61672114 and in part by the Research Grants from Macau under Grant MYRG2015-00049-FST, Grant MYRG2015-00050-FST, Grant RDG009/FST-TYY/2012, and Grant 008-2014-AMJ.

Publisher Copyright:
© 1980-2012 IEEE.

Other keywords

  • Difficultly separable samples (DSSs)
  • easily separable samples (ESSs)
  • highly confident set
  • multiple 1-D interpolation
  • principal component (PC)-based smooth ordering

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