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

doi:10.1109/36.655328

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

1998-01-01

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DOI：10.1109/36.655328

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Classification for high-dimensional remotely sensed data generally requires a large set of data samples and enormous processing time, particularly for hyperspectral image data, Hat 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 siege is to design a fast recursive MLC (RMLC) so as to achieve computational efficiency, The experimental results shelf 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.

Keywords:

Band selection (BS)

canonical analysis (CA)

principal components analysis (PCA)

recursive ML classifier (MLC)

Winograd's identity

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

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