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A Novel Spatial Analysis Method for Remote Sensing Image Classification

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Abstract

A new and efficient classification model is introduced in this paper. The proposed model enjoys the information of null space of within-class and range space of within-class. And the proposed model aims at defining a reliable spatial analysis criterion for the remote sensing image, taking advantage of the differences in different areas. Finally, by incorporating fisher linear discriminant analysis and support vector machine (or K-nearest neighbor) classifier among image pixels, the model obtained more accurate classification results.

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Acknowledgments

The authors are very grateful to the editor and anonymous referees reviews for their valuable comments and helpful suggestions. In addition, this work is supported by National Natural Science Foundation of P.R. China (Grant No. 61271386), and the Graduates’ Research Innovation Program of Higher Education of Jiangsu Province of P.R. China (Grant No. CXZZ13-0239).

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Authors and Affiliations

  1. College of Computer and Information Engineering, Hohai University, Nanjing, 210098, People‘s Republic of China

    Jianqiang Gao & Lizhong Xu

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  1. Jianqiang Gao
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  2. Lizhong Xu
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Correspondence to Jianqiang Gao.

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Gao, J., Xu, L. A Novel Spatial Analysis Method for Remote Sensing Image Classification. Neural Process Lett 43, 805–821 (2016). https://doi.org/10.1007/s11063-015-9447-0

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