Spectral clustering for water body spectral types analysis

Leping Huang; Shijin Li; Lingli Wang; Deqing Chen

doi:10.1117/12.2294512

15 November 2017 Spectral clustering for water body spectral types analysis

Leping Huang, Shijin Li, Lingli Wang, Deqing Chen

Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106052Y (2017) https://doi.org/10.1117/12.2294512
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China

Abstract

In order to study the spectral types of water body in the whole country, the key issue of reservoir research is to obtain and to analyze the information of water body in the reservoir quantitatively and accurately. A new type of weight matrix is constructed by utilizing the spectral features and spatial features of the spectra from GF-1 remote sensing images comprehensively. Then an improved spectral clustering algorithm is proposed based on this weight matrix to cluster representative reservoirs in China. According to the internal clustering validity index which called Davies-Bouldin(DB) index, the best clustering number 7 is obtained. Compared with two clustering algorithms, the spectral clustering algorithm based only on spectral features and the K-means algorithm based on spectral features and spatial features, simulation results demonstrate that the proposed spectral clustering algorithm based on spectral features and spatial features has a higher clustering accuracy, which can better reflect the spatial clustering characteristics of representative reservoirs in various provinces in China - similar spectral properties and adjacent geographical locations.

Citation Download Citation

Leping Huang, Shijin Li, Lingli Wang, and Deqing Chen "Spectral clustering for water body spectral types analysis", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106052Y (15 November 2017); https://doi.org/10.1117/12.2294512

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