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Adaptive pixel unmixing based on a fuzzy ARTMAP neural network with selective endmembers

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Abstract

Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model.

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Acknowledgments

The research is funded by the Open Research Fund of Key Laboratory of Digital Earth, Center for Earth Observation and Digital Earth, Chinese Academy of Sciences (2012LDE015); the Natural Science Foundation of China (61372153); the Natural Science Foundation of Hubei Province, China (2014CFA052); the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (CUGL140410); the Key Laboratory of Mapping from Space, National Administration of Surveying, Mapping and Geoinformation (K201302); the Open Research Fund of Key Laboratory of Disaster Reduction and Emergency Response Engineering of the Ministry of Civil Affairs under grant No. LDRERE20120204; the Scientific Research Foundation for NASG Key Laboratory of Land Environment and Disaster Monitoring (No. LEDM2012B05); the Key Laboratory of Agricultural Information Technology, Ministry of Agriculture, Beijing, 100081, China (2012005); and Jiangxi Province Key Lab for Digital Land (DLLJ201316).

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Authors and Affiliations

  1. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan, 430079, China

    Ke Wu, Xianmin Wang & Ruiqing Niu

  2. Department of Resources and Environmental Science, Hubei University, Wuhan, 430062, China

    Lifei Wei

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  1. Ke Wu
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  2. Lifei Wei
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  3. Xianmin Wang
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  4. Ruiqing Niu
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Correspondence to Ke Wu.

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Communicated by Y.-S. Ong.

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Wu, K., Wei, L., Wang, X. et al. Adaptive pixel unmixing based on a fuzzy ARTMAP neural network with selective endmembers. Soft Comput 20, 4723–4732 (2016). https://doi.org/10.1007/s00500-015-1700-y

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