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\iteman{io-port 05487528}
\itemau{Chen, Yen-Wei; Han, Xian-Hua}
\itemti{Classification of high-resolution satellite images using supervised locality preserving projections.}
\itemso{Lovrek, Ignac (ed.) et al., Knowledge-based intelligent information and engineering systems. 12th international conference, KES 2008, Zagreb, Croatia, September 3--5, 2008. Proceedings, Part II. Berlin: Springer (ISBN 978-3-540-85564-4/pbk). Lecture Notes in Computer Science 5178. Lecture Notes in Artificial Intelligence, 149-156 (2008).}
\itemab
Summary: We proposed a new method based on supervised locality preserving projections (SLPP) for classification of high resolution satellite images. Compared with other subspace methods such as PCA and ICA, SLPP can preserve local geometric structure of data and enhance within-class local information. The proposed method has been successfully applied to IKONOS images and experimental results show that the proposed SLPP based method outperform ICA-based method. The proposed method can be practically incorporated into a GIS system.
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\itemcc{}
\itemut{High-resolution satellite image; supervised; locality preserving projections; classification; multi-spectral image}
\itemli{doi:10.1007/978-3-540-85565-1\_19}
\end