Spectral unmixing approach in hyperspectral remote sensing: a tool for oil palm mapping
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.
“El sector palmero se ha consolidado en Colombia como un gremio responsable,” in El palmicultor, 2019, pp. 6–7.
A. Drenth, G. A. Torres, and G. M. López, “Phytophthora palmivora, la causa de la Pudrición del cogollo en la palma de aceite,” Rev. Palmas, vol. 34, no. 1, pp. 87–94, Jan. 2013.
L. F. Gómez, “Actualícese: todo lo que debe saber acerca de la PC--Hoja clorótica en Zona Norte,” Fedepalma, vol. 540, pp. 17–19, Feb. 2017.
P. S. Thenkabail, I. Mariotto, M. K. Gumma, E. M. Middleton, D. R. Landis, and K. F. Huemmrich, “Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion/EO-1 Data,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 6, no. 2, pp. 427–439, Apr. 2013. https://doi.org/10.1109/JSTARS.2013.2252601.
P. Ghamisi et al., “Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art,” IEEE Geosci. Remote Sens. Mag., vol. 5, no. 4, pp. 37–78, Dec. 2017. https://doi.org/10.1109/MGRS.2017.2762087.
M. Teke, H. S. Deveci, O. Haliloglu, S. Z. Gurbuz, and U. Sakarya, “A short survey of hyperspectral remote sensing applications in agriculture,” in 2013 6th International Conference on Recent Advances in Space Technologies (RAST), 2013. pp. 171–176. https://doi.org/10.1109/RAST.2013.6581194.
K. Jusoff and M. Pathan, “Mapping of Individual Oil Palm Trees Using Airborne Hyperspectral Sensing: An Overview,” Appl. Phys. Res., vol. 1, no. 1, p. 15, Apr. 2009. https://doi.org/10.5539/apr.v1n1p15.
H. Z. M. Shafri, M. I. Anuar, I. A. Seman, and N. M. Noor, “Spectral discrimination of healthy and Ganoderma -infected oil palms from hyperspectral data,” Int. J. Remote Sens., vol. 32, no. 22, pp. 7111–7129, Nov. 2011. https://doi.org/10.1080/01431161.2010.519003.
M. A. Izzuddin, A. S. Idris, N. M. Nisfariza, and B. Ezzati, “Spectral based analysis of airborne hyperspectral remote sensing image for detection of ganoderma disease in oil palm,” in Proceedings of Conference on Biological and Environmental Science (BIOES 2015), Phuket. 2015. pp. 13–20.
C. C. Lelong et al., “Evaluation of Oil-Palm Fungal Disease Infestation with Canopy Hyperspectral Reflectance Data,” Sensors, vol. 10, no. 1, pp. 734–747, Jan. 2010. https://doi.org/10.3390/s100100734.
J. M. Bioucas-Dias et al., “Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 5, no. 2, pp. 354–379, Apr. 2012. https://doi.org/10.1109/JSTARS.2012.2194696.
B. Somers, G. P. Asner, L. Tits, and P. Coppin, “Endmember variability in Spectral Mixture Analysis: A review,” Remote Sens. Environ., vol. 115, no. 7, pp. 1603–1616, Jul. 2011. https://doi.org/10.1016/j.rse.2011.03.003.
A. Zare and K. C. Ho, “Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing,” IEEE Signal Process. Mag., vol. 31, no. 1, pp. 95–104, Jan. 2014. https://doi.org/10.1109/MSP.2013.2279177.
A. Halimi, P. Honeine, and J. M. Bioucas-Dias, “Hyperspectral Unmixing in Presence of Endmember Variability, Nonlinearity, or Mismodeling Effects,” IEEE Trans. Image Process., vol. 25, no. 10, pp. 4565–4579, Oct. 2016. https://doi.org/10.1109/TIP.2016.2590324.
T. Uezato, R. J. Murphy, A. Melkumyan, and A. Chlingaryan, “A Novel Spectral Unmixing Method Incorporating Spectral Variability Within Endmember Classes,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 5, pp. 2812–2831, May 2016. https://doi.org/10.1109/TGRS.2015.2506168.
P.-A. Thouvenin, N. Dobigeon, and J.-Y. Tourneret, “Hyperspectral Unmixing With Spectral Variability Using a Perturbed Linear Mixing Model,” IEEE Trans. Signal Process., vol. 64, no. 2, pp. 525–538, Jan. 2016. https://doi.org/10.1109/TSP.2015.2486746.
J. M. Bioucas-Dias and J. M. P. Nascimento, “Hyperspectral Subspace Identification,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 8, pp. 2435–2445, Aug. 2008. https://doi.org/10.1109/TGRS.2008.918089.
J. P. Kerekes and J. E. Baum, “Hyperspectral imaging system modeling,” Lincoln Lab. J., vol. 14, no. 1, pp. 117–130, Jan. 2003.
B. Rasti, M. O. Ulfarsson, and J. R. Sveinsson, “Hyperspectral Subspace Identification Using SURE,” IEEE Geosci. Remote Sens. Lett., vol. 12, no. 12, pp. 2481–2485, Dec. 2015. https://doi.org/10.1109/LGRS.2015.2485999.
M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, “Sparse Unmixing of Hyperspectral Data,” IEEE Trans. Geosci. Remote Sens., vol. 49, no. 6, pp. 2014–2039, Jun. 2011. https://doi.org/10.1109/TGRS.2010.2098413.
J. M. P. Nascimento and J. M. B. Dias, “Vertex component analysis: a fast algorithm to unmix hyperspectral data,” IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 898–910, Apr. 2005. https://doi.org/10.1109/TGRS.2005.844293.
J. M. Bioucas-Dias, “A variable splitting augmented Lagrangian approach to linear spectral unmixing,” in 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. pp. 1–4. https://doi.org/10.1109/WHISPERS.2009.5289072.
J. M. Bioucas-Dias and M. A. T. Figueiredo, “Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing,” in 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010. vol. 1. https://doi.org/10.1109/WHISPERS.2010.5594963.
M.-D. Iordache, J. M. Bioucas-Dias, and A. Plaza, “Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing,” IEEE Trans. Geosci. Remote Sens., vol. 50, no. 11, pp. 4484–4502, Nov. 2012. https://doi.org/10.1109/TGRS.2012.2191590.
C.-I. Chang and Q. Du, “Estimation of Number of Spectrally Distinct Signal Sources in Hyperspectral Imagery,” IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 608–619, Mar. 2004. https://doi.org/10.1109/TGRS.2003.819189.
S. J. K. Pedersen, “Circular hough transform,” Aalborg Univ. Vision, Graph. Interact. Syst., vol. 123, no. 6, Nov. 2007.
Copyright (c) 2019 TecnoLógicas
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This journal uses Crossref's Cited-By and Reference Linking, so that we can display the citations registered in Crossref here.
This document does not have Crossref citations yet.