Spectral unmixing approach in hyperspectral remote sensing: a tool for oil palm mapping

Keywords: Hyperspectral, Spectral Variability, Unmixing, Endmember, Abundance, Oil palm


Oil palm plantations typically span large areas; therefore, remote sensing has become a useful tool for advanced oil palm monitoring. This work reviews and evaluates two approaches to analyze oil palm plantations based on hyperspectral remote sensing data: linear spectral unmixing and spectral variability. Moreover, a computational framework based on spectral unmixing for the estimation of fractional abundances of oil palm plantations is proposed in this study. Such approach also considers the spectral variability of hyperspectral image signatures. More specifically, the proposed computational framework modifies the linear mixing model by introducing a weighting vector, so that the spectral bands that contribute the least to the estimation of erroneous fractional abundances can be identified. This approach improves palm detection as it allows to differentiate them from other materials in terms of fractional abundances. Experimental results obtained from hyperspectral remote sensing data in the range 410-990 nm show improvements of 8.18 % in User Accuracy (Uacc) in the identification of oil palms by the proposed framework with respect to traditional unmixing methods. Thus, the proposed method achieved a 95% Uacc. This confirms the capabilities of the proposed computational framework and facilitates the management and monitoring of large areas of oil palm plantations.

Author Biographies

Hector Vargas, Universidad Industrial de Santander, Colombia

MSc en Ingeniería Electrónica, Departamento de Ingeniería Electrónica, Universidad Industrial de Santander, Bucaramanga, Colombia, hector.vargas@correo.uis.edu.co

Ariolfo Camacho Velasco, Universidad Industrial de Santander,Colombia

MSc en Ingeniería de Sistemas e Informática, Departamento de Ingeniería de Sistemas, Universidad Industrial de Santander, Bucaramanga, Colombia, ariolfo.camacho@correo.uis.edu.co 

Henry Arguello, *, Universidad Industrial de Santander, Colombia

PhD en Ingeniería Eléctrica y Computación, Departamento de Ingeniería de Sistemas, Universidad Industrial de Santander, Bucaramanga, Colombia, henarfu@uis.edu.co
*Corresponding author.


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How to Cite
H. Vargas, A. Camacho Velasco, and H. Arguello, “Spectral unmixing approach in hyperspectral remote sensing: a tool for oil palm mapping”, TecnoL., vol. 22, no. 45, pp. 129-143, May 2019.


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