Abstract
Based on the search strategy of representative bands in Hyperspectral Imagery, various existing unsupervised band selection approaches are mainly classified into two parts: ranking-based and clustering-based ones. Recently, a fast density peak-based clustering (abbreviated as FDPC) algorithm has been proposed. The product of two factors (the computation of local density and intra-cluster distance) is sorted in decreasing order and cluster centers are recognized as points with anomalously large values, hence the FDPC algorithm can be considered as a ranking-based clustering method. In this paper, the FDPC algorithm has been modified to make it suitable for hyperspectral band selection by weighting the normalized local density and intra-cluster distance. It is called a weighted fast density peak-based clustering (W-FDPC) method. Experimental results demonstrate that the bands selected by W-FDPC approach can achieve higher overall classification accuracies than FDPC and other state-of-the-art band selection techniques.
This work was jointly supported by grants from National Natural Science Foundation of China (61271022 and 61272050), Guangdong Foundation of Outstanding Young Teachers in Higher Education Institutions (Yq2013143), and Shenzhen Scientific Research and Development Funding Program (JCYJ20140418095735628).
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Jia, S., Tang, G., Hu, J. (2015). Band Selection of Hyperspectral Imagery Using a Weighted Fast Density Peak-Based Clustering Approach. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_6
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DOI: https://doi.org/10.1007/978-3-319-23989-7_6
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