A fast two-stage classification method for high-dimensional remote sensing data

Te Ming Tu, Chin Hsing Chen, Jiunn Lin Wu, Chein I. Chang

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


Classification for high-dimensional remotely sensed data generally requires a large set of data samples and enormous processing time, particularly for hyperspectral image data. In this paper, we present a fast two-stage classification method composed of a band selection (BS) algorithm with feature extraction/selection (FSE) followed by a recursive maximum likelihood classifier (MLC). The first stage is to develop a BS algorithm coupled with FSE for data dimensionality reduction. The second stage is to design a fast recursive MLC (RMLC) so as to achieve computational efficiency. The experimental results show that the proposed recursive MLC, in conjunction with BS and FSE, reduces computing time significantly by a factor ranging from 30 to 145, as compared to the conventional MLC.

Original languageEnglish
Pages (from-to)182-191
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number1
Publication statusPublished - 1998

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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