Applications and challenges of hyperspectral remote sensing in the colombian geology


  • Ariolfo Camacho-Velasco Universidad Industrial de Santander
  • Cesar Augusto Vargas-García Universidad Industrial de Santander
  • Fernando Antonio Rojas-Morales Universidad Autónoma de Bucaramanga
  • Sergio Fernando Castillo-Castelblanco Universidad Politécnica De Madrid
  • Henry Arguello-Fuentes Universidad De Delaware



remote sensing, hyperspectral imaging, spectral signature, geology, target detection algorithms


Remote Sensing (RS) is a data acquisition technique that requires no physical contact with the scene, through the use of sensors on aerial platforms. These sensors capture information in the electromagnetic spectrum different ranges, including the visible ones (VIS), the near infrared (NIR) and short wave range infrared (SWIR). Taking into account that each material present in a scene has different spectral characteristics, it is possible to analyze the spectral signatures and to use their identification and/or classification algorithms. Hyperspectral Image (HSI) remotely sensed in hundreds of spectral bands. HSI is important in different areas such as geology, mineralogy, agronomy, geography, environment, among others. However, the high volume of literature spread into different lines (RS, HSI and geology) makes it difficult to access and analyze it.

This paper presents a summary of concepts, principles, and mathematical foundations, including RS research and trends, highlighting its development and challenges in Colombia, and a HSI case use in the Colombian geology, which shows the detection capability of the Hyperion hyperspectral sensor, located in the EQ-1 Satellite for geological mapping, in a test site, at the Girón town northwest of the Santander, Colombia. The results of these evaluations are correlated with the geological information obtained by analysis of X-ray diffraction (XRD), performed on the samples taken from the studied area.


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J. A. Richards y X. Jia, Remote Sensing Digital Image Analysis, New York, Berlin: Springer-Verlag, 2006.

N. M. Nasrabadi, “Hyperspectral Target Detection”, IEEE Signal Processing Magazine, vol. 31, nº 1, pp. 34-44, 2014.

W.-K. Ma, J. M. Bioucas-Dias, J. Chanussot y P. Gader, “Signal and image processing in hyperspectral remote sensing”, IEEE Signal Processing Magazine, vol. 31, nº 1, pp. 22-23, 2014.

D. Manolakis, D. Marden y G. A.-. Shaw, “Hyperspectral Image Processing for Automatic Target Detection Applications”, Lincoln Laboratory Journal, vol. 14, nº 1, pp. 79-116, 2003.

L. Homolová, Z. Malenovský, J. G. Clevers y G. Garcia, “Review of optical-based remote sensing for plant trait mapping”, Ecological Complexity, vol. 15, nº 1, pp. 1-16, 2013.

E. Bastidas y J.A. Carbonell, “Caracterización espectral y mineralógica de los suelos del valle del río Cauca por espectroscopía visible e infrarroja (400-2.500 nm)”, Agronomía Colombiana, vol. 28, nº 2, pp. 291-301, 2010.

D. F. Correa y E. Posada, “The social and economic benefits of Remote Sensing and Earth Observation Satellite Systems”, Tecnologías geoespaciales al servicio del desarrollo territorial, vol. 49, pp. 15-26, 2013.

M. Zhang, Z. Qin, X. Liu y S. L. Ustin, “Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing”, International Journal of Applied Earth Observation and Geoinformation, vol. 4, nº 4, p. 295–310, 2003.

S. M. Arafat, M. A. Aboelghar y E. F. Ahmed, “Crop Discrimination Using Field Hyper Spectral Remotely Sensed Data”, Advances in Remote Sensing, vol. 2, pp. 63-70, 2013.

T. H. Kurz, S. J. Buckley y J. A. Howell, “Close range hyperspectral imaging integrated with terrestrial lidar scanning applied to rock characterization at centimetre scale”, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 39, nº 5, pp. 417-422, 2012.

USGS. GEOLOGICAL SURVEY, “”,U.S. GEOLOGICAL SURVEY, 07-05-2014. [En línea]. Disponible:

Earth Observing 1, EO-1, “Earth Observing 1 (EO-1) / Sensor Hyperion”, 13-12-2011. [En línea]. Disponible: [Último acceso: 06-08-2014].

Digital Globe, 07-05-2014. [En línea]. Disponible:

G. A. Shaw y H.-H. Burker, “Spectral Imaging for Remote Sensing”, Lincoln Laboratory Journal, vol. 14, nº 1, pp. 3-28, 2003.

J. B. Campbell, Introduction to Remote Sensing, Edition Seven, New York: Guilford Press, 2007.

F. Kruse, “Advances in Hyperspectral Remote Sensing for Geologic Mapping and Exploration”, Proceedings 9th Australasian Remote Sensing Conference, Sydney, Australia, 1998.

M. Labrador García, J. A. Évora Brondo y M. A. Pérez, Satélites de Teledetección para la Gestión del Territorio, Canarias, España: Consejería de Agricultura, Ganadería, Pesca y Aguas del Gobierno de Canaria, 2012.

A. C. Watts, V. G. Ambrosia y E. A. Hinkley, “Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use”, Remote Sensing, vol. 4, nº 1, pp. 1671-1692, 2012.

C. Chen, Remote sensing: models and methods for image processing, 3ra ed., New York, FL: Crc Press Taylor and Francis Group, 2006.

F. Ritchin, After Photography, New York: W. W. Norton & Company, 2008.

W. C. Van Den Hoonaard, Map Wordlsa History of Women in Cartography, Ontario, Canada: Wilfrid Laurier University Press, 2013.

NASA, “”, 24 10 2014. [En línea]. Disponible:

C. Pohl y J. L. Van Genderen, “Multisensor image fusion in remote sensing: concepts, methods and applications”, International Journal of Remote Sensing, vol. 19, nº5,pp. 823-854, 1998.

IGAC, “Informe 2012-2013 Instituto Geográfico Agustin Codazzi, IGAC”, Oficina de Difusión y Mercadeo de información, IGAC, Bogotá, 2013.

E. Adam, O. Mutanga y D. Rugege, “Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review”, Wetlands Ecology and Management, vol. 18, nº 3, pp. 281-296, 2010.

T. V. King y R. N. Clark, Verification of Remotely Sensed Data, in Remote Sensing for Site Characterization, Berlin: Springer, pp. 59-61, 2000.

H. Kwon y N. M. Nasrabadi, “A comparative analysis of kernel subspace target detectors for hyperspectral imagery”, EURASIP Journal on Advances Signal Process, Article ID 29250, 13 pages, 2007.

A. M. Baldridge, S. J. Hook, C. I. Grove y G. Rivera, “The ASTER spectral library version 2.0”, Remote Sensing of Environment, vol. 113, nº 4, pp. 711-715, 2009.

U.S. Geological Survey (USGS), “U.S. Geological Survey (USGS) Libraries Program” 30-10-2014. [En línea]. Disponible:

J. W. Salisbury, L. S. Walter, N. Vergo y D. M. D’Aria, Infrared (2.1-25 micrometers) Spectra of Minerals: Johns Hopkins University, Maryland: The Johns Hopkins University Press, 1991.

S. Hook, C. Grove y E. Paylor, “Laboratory reflectance spectra of 160 minerals, 0.4 to 2.5 Micrometers: JPL”, JPL Publication, pp. 152-153, 1992.

R. A. Schowengerdt, Remote sensing: models and methods for image processing (3rd ed.), Burlington. USA: Academic Press, 2007.

M. K. Griffin, S. M. Hsu, H.-h. K. Burke, S. M. Orloff y C. A. Upham, “Examples of EO-1 Hyperion Data Analysis”, Lincoln Laboratory Journal, vol. 15, nº 2, pp. 271-298, 2005.

U.S. Geological Survey (USGS), Hyperion Level 1G (L1GST) Product Output Files Data Format Control Book (DFCB), USGS, Sioux Falls, South Dakota, USA, Disponible: 2006.

NASA-Stuart Frye, “GeoBPMS”, 14-04-2015. [En línea]. Disponible:

J. M. Royero y J. Clavijo, Mapa Geológico Generalizado del Departamento de Santander, Bogotá: Ingeominas, 2001.

B. Datt, T. R. McVicar, T. G. Van Niel, D. L. Jupp y J. S. Pearlman, “Preprocessing EO-1 Hyperion Hyperspectral Data to Support the Application of Agricultural Indexes,” IEEE Transactions On Geoscience And Remote Sensing, vol. 41, nº 6, pp. 1246-1259, 2003.

Y. Smara, Z. Hamadache y S. Chouaf, “Preprocessing EO-1 Hyperion hyperspectral data applied to forests and vegetation classification”, de ForestSAT conference 2014, Riva del Garda, Italia, 2014.

Exelis, Inc, Atmospheric Correction Module: QUAC and FLAASH, 2009. [En línea]. Disponible:

F. A. Kruse, A. B. Lefkoff y J. W. Boardman, “The spectral image processing system (SIPS) interactive visualization and analysis of imaging spectrometer data”, Remote Sensing Environmental, vol. 44, nº 2, pp. 145-163, 1993.

F. van der Meer, “The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery”, International Journal of Applied Earth Observation and Geoinformation, vol. 8, nº 1, pp. 3-17, 2006.

M. Mounkaila, Spectral and Mineralogical Properties of Potential Dust Sources on a Transect from the Bodélé Depression (central Sahara) to the Lake Chad in the Sahel, Vol. 78, Univ. Hohenheim, 2006.

A. Chudnovsky, A. Kostinski, L. Herrmann, I. Koren, G. Nutesku y E. Ben-Dor, “Hyperspectral space borne imaging of dustladen flows: Anatomy of Saharan dust storm from the Bodélé Depression”, Remote Sensing of Environment, vol. 115, nº 1, pp. 1013-1024, 2011.

J. M. Nascimento y J. M. Bioucas, “Vertex Component Analysis: A Fast Algorithm to Unmix Hyperspectral Data”, IEEE Transactions On Geoscience And Remote Sensing, vol. 43, nº 4, pp. 898-910, 2005.

J. Boardman, F. Kruse y R. Green, “Mapping target signatures via partial unmixing of AVIRIS data”, Fifth JPL Airborne Earth Science Workshop, vol. 95, nº 1, pp. 23-26, 1995.

A. J. Jerri, “The Shannon sampling theorem; Its various extensions and applications: A tutorial review”, Proceedings of the IEEE, vol. 65, nº 11, pp. 1565-1596, 1977.

A. Ramirez, H. Arguello, G. R. Arce y B. M. Sadler, “Spectral Image Classification from Optimal Code-Aperture Compressive Measurements”, IEEE Transactions on Geoscience and Remote Sensing, vol. 52, nº 6, pp. 3299-3309, 2014.

D. F. Galvis, Y. H. Mejía y H. Arguello, “Efficient reconstruction of Raman spectroscopy imaging based on compressive sensing”, Dyna, vol. 81, nº 188, pp. 116-124, 2014.

D. Brady, Optical Imaging and Spectroscopy, Durham, North Carolina, USA:Wyley, 2009.

S. Gottesman y E. Fenimore, “New family of binary arrays for coded aperture imaging”, Applied Optics, vol. 28, nº 20, pp. 4344–4352, 1989.

H. Arguello, H. Rueda, Y. Wu, W. Prather y G. Arce, “Higher-order computational model for coded aperture spectral imaging”, Applied Optics, vol. 56, nº 10, pp. D12–D21, 2013.

G. Arce y H. Arguello, “Rank minimization code aperture design for spectrally”, IEEE Trans. image Process, vol. 22, nº 3, pp. 941–954, 2013.

H. F. Rueda, A. Parada Mayorga y H. Arguello, “Spectral resolution enhancement of hyperspectral imagery by a multipleaperture compressive optical imaging system”, Ingeniería e Investigación, vol. 34, nº 3, pp. 50-55, 2014.

A. Plaza, J. Plaza, A. Paz y S. Sánchez, “Parallel Hyperspectral Image and Signal Processing”, IEEE Signal Processing Mag, vol. 28, nº 3, pp. 119-126, 2011.

A. J. Plaza y C.-I. Chang, High Performance Computing in Remote Sensing, New York: Chapman & Hall, 2008.

J. M. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. M. Nasrabadi y J. Chanussot, “Hyperspectral Remote Sensing Data Analysis and Future Challenges”, IEEE Geoscience and remote sensing magazine, vol. 1, nº 2, pp. 6-36, 2013.

Q. Tong, Y. Xue y L. Zhang, “Progress in Hyperspectral Remote Sensing Science and Technology in China Over the Past Three Decades”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, nº 1,pp. 70-91, 2014.

Sugianto, R. Merton y S. Laffan, “An Overview of the CHRIS/PROBA Mission: A New Generation of Multiangle Hyperspectral Remote Sensing and Its Application to Agriculture” nºTS22.2, New Development and Applications for Imagery Conference, Jakarta, Indonesia, 2004.

AngloGold Ashanti, “Anglogold Ashanti Colombia”, 28-01-2015. [En línea]. Disponible:

ANH, Agencia Nacional de Hidrocarburos, “ANH”, 01-02-2015. [En línea]. Disponible:

Y. B. López, Metodología para identificar cultivos de coca mediante análisis de parámetros red edge y espectroscopia de imágenes, Tesis, Universidad Nacional de Colombia, Bogotá,

Ministerio de Educación Colombia, “Centro Virtual de Noticias de la Educación”, 04 02 2015. [En línea]. Disponible:

ONU-SPIDER, “Oficina de Apoyo Regional de ONU-SPIDER”, 26 03 2014. [En línea]. Disponible:

I. Lizarazo, “Vegetation condition assessment using proximal and remote sensors”, 27 10 2013. [En línea]. Disponible: http://www.



How to Cite

Camacho-Velasco, A., Vargas-García, C. A., Rojas-Morales, F. A., Castillo-Castelblanco, S. F., & Arguello-Fuentes, H. (2015). Applications and challenges of hyperspectral remote sensing in the colombian geology. Revista Facultad De Ingeniería, 24(40), 17–29.