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The comparison of PCA and discrete rough set for feature extraction of remote sensing image classification – A case study on rice classification, Taiwan

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An Erratum to this article was published on 05 August 2008

Abstract

Texture information offers an extensive solution for image classification by providing better accuracy of image information. However, huge amounts of improper additional texture information may result in a chaotic state, and this leads to uncertainty in the classification process. Considerable portion of earlier works have been carried out through the generally acknowledged procedure of Principal Components Analysis (PCA). However, the PCA method has flaws in the area of influenced and non- influenced attributes. On the whole, whether PCA provides an effective solution to determine the value of knowledge rule in image information still remains a question. This study proposes an innovative method, called Discrete Rough Set method, as a tool for image classification. This study focuses on two crucial issues: (1) The core attributes of the target categories in image classification are systematically analyzed while eliminating surplus attributes rationally; (2) The unique point of each attribute, which influenced the target categories, is successfully found. This is a crucial aspect that is very helpful for the construction of decision rule. Finally, in this study we utilized the expert knowledge classifier and the overall accuracy of Discrete Rough Set (96.67%) exceeds that of the conventional PCA (86.00%) of paddy rice area evaluation from Quickbird image. This result shows that the appropriate classification knowledge can be presented by Discrete Rough Set, and this information can effectively improve the accuracy of image classification.

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Authors and Affiliations

  1. GIS Center, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, Taichung, Taiwan, 40724, Republic of China

    T. C. Lei & T. Y. Chou

  2. Department of Information Management, Ling Tung University, No.1 Ling Tung Road, Taichung, Taiwan, Republic of China

    S. Wan

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  1. T. C. Lei
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  2. S. Wan
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  3. T. Y. Chou
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Correspondence to T. C. Lei.

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An erratum to this article can be found at http://dx.doi.org/10.1007/s10596-008-9097-7

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Lei, T.C., Wan, S. & Chou, T.Y. The comparison of PCA and discrete rough set for feature extraction of remote sensing image classification – A case study on rice classification, Taiwan. Comput Geosci 12, 1–14 (2008). https://doi.org/10.1007/s10596-007-9057-7

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