TY - JOUR
T1 - A fast two-stage classification method for high-dimensional remote sensing data
AU - Tu, Te Ming
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: [email protected]). 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
Y1 - 1998
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.
AB - 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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U2 - 10.1109/36.655328
DO - 10.1109/36.655328
M3 - Article
AN - SCOPUS:0031646486
SN - 0196-2892
VL - 36
SP - 182
EP - 191
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 1
ER -