Cespe UnB

Editorial Assistants:
W. Abrahão
G. Oliveira
L. Salgueiro

Editorial Technical Support:
D. H. Diaz
M. A. Gomez
J. Barbosa

Editorial management and production:

95/105= 0.91


Remote Sensing Image Processing Functions in Lua Language

doi: 10.6062/jcis.2017.08.03.0133

(Free PDF)


R. F. B. Marujo, L. M. G. Fonseca, T. S. Korting, H.N. Bendini, G. R. Queiroz, L. Vinhas and K. R. Ferreira


Geographic Information Systems users without appropriate programming skills perform repetitive tasks through Graphical User Interfaces when manipulating large datasets, such as remote sensing imagery. Scripting languages, such as Lua, make the process easier for those users, due to their higher abstraction and simplicity, than the more complex programming languages such as C or C++. Based on that, this paper describes a high-level, open source programming environment for remote sensing image processing. We present a Lua API (Application Programming Interface) that provides image processing functions of the geospatial library TerraLib. This API allows users to easily and efficiently create new algorithm prototype for remote sensing images using the high-level programming language Lua. To demonstrate this API, we show an application to fill gaps in Landsat-7 images using a multiscale segmentation approach.


Elastic optical networks, Routing and spectrum assignment, Blocking probability.


[1] Exelis Visual Information Solutions ENVI Users Guide. Technical Report September, Boulder, Colorado. 2004.

[2] BOWMAN, K. P. An Introduction to Programming with IDL: Interactive Data Language. Academic Press. 304p. 2005

[3] CAMARA, G., SOUZA, R. C. M., FREITAS, U. M.; GARRIDO, J. Spring: Integrating remote sensing ˆ and gis by object-oriented data modelling. Computers and Graphics, v.20, n.3, p.395-403. 1996.

[4] OUSTERHOUT, J. Scripting: higher level programming for the 21st Century. Computer, v.31, n.3, p.23-30. 1998.

[5] REDDY, M. API Design for C++. Morgan Kaufmann. 472p. 2011.

[6] IERUSALIMSCHY, R., FIGUEIREDO, L. H.; CELES, W. Passing a Language through the Eye of a Needle. Queue, v.9, n.5, p.38-43. 2011.

[7] IERUSALIMSCHY, R. Programming in Lua. Lua Org, 3rd edition. 347p. 2013.

[8] CAMARA, G., VINHAS, L., FERREIRA, K. R., QUEIROZ, G. R. D., SOUZA, R. C. M. D., MON- ˆ TEIRO, A. M. V., CARVALHO, M. T. D., CASANOVA, M. A., AND FREITAS, U. M. D. TerraLib: An Open Source GIS Library for Large-Scale Environmental and Socio-Economic Applications. In: HALL G.B., LEAHY M.G. (eds) Open Source Approaches in Spatial Data Handling. Advances in Geographic Information Science, vol 2. Springer, Berlin, Heidelberg, p.247-270. 2008.

[9] MAXWELL, S. K., SCHMIDT, G. L., STOREY, J. C., MAXWELL, S., K., SCHMIDT, G., L., STOREY; J., C. A multi-scale segmentation approach to filling gaps in Landsat ETM+ SLC-off images. International Journal of Remote Sensing, v.28, n.23, p.5339-5356. 2007.

[10] GONZALEZ-AGULLA, E., OTERO-MURAS, E., GARCIA-MATEO, C.; ALBA-CASTRO, J. L. A multiplatform Java wrapper for the BioAPI framework. Computer Standards and Interfaces, v.31, n.1, p.186-191. 2009.

[11] ERICSON, K.; PALLICKARA, S. Adaptive heterogeneous language support within a cloud runtime. Future Generation Computer Systems, v.28, n.1, p.128-135. 2012.

[12] LI, M., WALKER, D. W., RANA, O. F.; HUANG, Y. Migrating legacy codes to distributed computing environments: A CORBA approach. Information and Software Technology, v.46, n.7, p.457-464. 2004.

[13] GDAL. GDAL - geospatial data abstraction library. 2010. URL: http://www.gdal.org. Accessed: November 21, 2016.

[14] GRASS Development Team. Geographic Resources Analysis Support System (GRASS GIS) Software, Version 7.0. Open Source Geospatial Foundation. 2016.

[15] NETELER, M., BOWMAN, M., LANDA, M.; METZ, M. GRASS GIS: a multi-purpose Open Source GIS. Environmental Modelling & Software, v.31, p.124-130. 2012.

[16] BIVAND, R. Using the RGRASS interface: Current status. OSGeo Journal, v.1, p. 36–38, May, 2007.

[17] BIVAND, R. spgrass6: Interface between GRASS 6 and R. R package version 0.8-9. 2016. Available: http://CRAN.R-project.org/package=spgrass6. Accessed: November 21, 2016.

[18] QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation. 2009.

[19] COHEN, W. B.; GOWARD, S. N. Landsats Role in Ecological Applications of Remote Sensing. BioScience, v.54, n.6, p.535-546. 2004.

[20] STOREY, J., SCARAMUZZA, P.; SCHMIDT, G. Landsat 7 Scan Line Corrector-Off Gap-Filled Product Gap-Filled Product Development. In: Pecora. Proceedings. v.16, p.23-27. 2005.

[21] TOBLER, W. R. A computer movie simulating urban growth in the Detroit region. Economic Geography, v.46, p.234-40. 1970.

[22] MAXWELL, S. Filling Landsat ETM+ SLC-off Gaps Using a Segmentation Model Approach. Photogrammetric Engineering & Remote Sensing, v.70, n.10, p.1109-1111. 2004.

[23] BEAZLEY, D. M. SWIG: an easy to use tool for integrating scripting languages with C and C++. In: Fourth USENIX Tcl/Tk Workshop Proceedings. p. 129-139, 1996.

[24] ZHU, Z.; WOODCOCK, C. E. Object-based cloud and cloud shadow detection in landsat imagery. Remote Sensing of Environment, v.118, p.83-94. 2012.

[24] ZHU, Z.; WOODCOCK, C. E. Object-based cloud and cloud shadow detection in landsat imagery. Remote Sensing of Environment, v.118, p.83-94. 2012.

[25] ZHU, Z., WANG, S.; WOODCOCK, C. E. Improvement and expansion of the fmask algorithm: cloud, cloud shadow, and snow detection for landsats 47, 8, and sentinel 2 images. Remote Sensing of Environment, v.159, p.269-277. 2015.

[26] JENKERSON, C. User guide: Earth resources observation and science (EROS) center science processing architecture (ESPA) on demand interface. 2013. URL: http://pubs.er.usgs.gov/publication/70057873. Accessed: November 21, 2016.

[27] ROUSE, J. W., HAAS, R. H., SCHELL, J. A.; DEERING, D. W. Monitoring vegetation systems in the Great Plains with ERTS. Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Symposium, v. 1, p. 48–62. 1974.

[28] BAATZ, M.; SCHAPE, A. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angewandte Geographische Informations verarbeitung XII. p.12–23. 2000.

[29] GAMANYA, R., DE MAEYER, P.; DAPPER, M. An automated satellite image classification design using object-oriented segmentation algorithms: A move towards standardization. Expert Systems with Applications, v.32, n.2, p.616-624. 2007.


Combining wavelets and linear spectral mixture model for MODIS satellite sensor time-series analysis
doi: 10.6062/jcis.2008.01.01.0005
Freitas and Shimabukuro(Free PDF)

Riddled basins in complex physical and biological systems
doi: 10.6062/jcis.2009.01.02.0009
Viana et al.(Free PDF)

Use of ordinary Kriging algorithm and wavelet analysis to understanding the turbidity behavior in an Amazon floodplain
doi: 10.6062/jcis.2008.01.01.0006
Alcantara.(Free PDF)

A new multi-particle collision algorithm for optimization in a high performance environment
doi: 10.6062/jcis.2008.01.01.0001
Luz et al.((Free PDF)

Reviewer Guidelines
(Under Construction)
Advertises Media Information