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

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

doi:10.1109/36.655328

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

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

Institute of Computer and Communication Engineering

Research output: Contribution to journal › Article › peer-review

49 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	182-191
Number of pages	10
Journal	IEEE Transactions on Geoscience and Remote Sensing
Volume	36
Issue number	1
DOIs	https://doi.org/10.1109/36.655328
Publication status	Published - 1998

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering
Earth and Planetary Sciences(all)

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title = "A fast two-stage classification method for high-dimensional remote sensing data",

abstract = "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.",

author = "Tu, {Te Ming} and Chen, {Chin Hsing} and Wu, {Jiunn Lin} and Chang, {Chein I.}",

note = "Funding Information: Manuscript received August 8, 1995; revised July 2, 1996. This work was supported by the National Science Council under Grants NSC 85-2213-E-006-066 and NSC 84-2213-E-006-086. T.-M. Tu is with the Department of Electrical Engineering, Chung Cheng Institute of Technology, Tahsi, Taoyuan, Taiwan 33509, R.O.C. (e-mail: tutm@cc04.ccit.edu.tw). C.-H. Chen and J.-L. Wu are with the Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan 70101, R.O.C. C.-I. Chang is with the Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland-Baltimore County, Baltimore, MD 21228-5398 USA. Publisher Item Identifier S 0196-2892(98)00034-5.",

year = "1998",

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AU - Chen, Chin Hsing

AU - Wu, Jiunn Lin

AU - Chang, Chein I.

N1 - Funding Information: Manuscript received August 8, 1995; revised July 2, 1996. This work was supported by the National Science Council under Grants NSC 85-2213-E-006-066 and NSC 84-2213-E-006-086. T.-M. Tu is with the Department of Electrical Engineering, Chung Cheng Institute of Technology, Tahsi, Taoyuan, Taiwan 33509, R.O.C. (e-mail: tutm@cc04.ccit.edu.tw). C.-H. Chen and J.-L. Wu are with the Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan 70101, R.O.C. C.-I. Chang is with the Department of Computer Science and Electrical Engineering, Remote Sensing Signal and Image Processing Laboratory, University of Maryland-Baltimore County, Baltimore, MD 21228-5398 USA. Publisher Item Identifier S 0196-2892(98)00034-5.

PY - 1998

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N2 - 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.

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