An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops

Computers and Electronics in Agriculture

Volumn 114, Issue , 2015, Pages 43-52

  Torres Sanchez J ^a   Lopez Granados F ^a   Pena J M ^a  

^a INSTITUTO DE AGRICULTURA SOSTENIBLE   (Spain)

Author keywords

Image segmentation; OBIA; Remote sensing; Segmentation parameters; Unmanned aerial vehicle; Unsupervised classification

Indexed keywords

ALGORITHMS; CAMERAS; CROPS; IMAGE ANALYSIS; IMAGE SEGMENTATION; PARAMETER ESTIMATION; PLANTS (BOTANY); REMOTE SENSING; UNMANNED AERIAL VEHICLES (UAV);

CLASSIFICATION ACCURACY; MULTI-SPECTRAL CAMERAS; NORMALIZED DIFFERENCE VEGETATION INDEX; OBIA; OBJECT BASED IMAGE ANALYSIS (OBIA); REMOTELY SENSED IMAGES; SEGMENTATION PROCEDURE; UNSUPERVISED CLASSIFICATION;

VEGETATION;

ALGORITHM; AUTOMATION; GERMINATION; HERB; HISTOGRAM; IMAGE ANALYSIS; INNOVATION; NDVI; PERFORMANCE ASSESSMENT; PRECISION AGRICULTURE; REMOTE SENSING; THRESHOLD; UNMANNED VEHICLE; WEED CONTROL;

HELIANTHUS; TRITICUM AESTIVUM; ZEA MAYS;

EID: 84926633657     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2015.03.019     Document Type: Article

References (22)

1. Addink E.A., de Jong S.M., Pebesma E.J. The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery. Photogramm. Eng. Remote Sens. 2007, 73:905-912.
2. Aplin P. On scales and dynamics in observing the environment. Int. J. Remote Sens. 2006, 27:2123-2140. 10.1080/01431160500396477.
3. Baatz, M., Schaepe, A., 2000. Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation (eCognition) [WWW Document]. <> (accessed 04.18.14). http://www.ecognition.cc/download/baatz_schaepe.pdf.
4. Blaschke T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 2010, 65:2-16. 10.1016/j.isprsjprs.2009.06.004.
5. Drăguţ L., Tiede D., Levick S.R. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inf. Sci. 2010, 24:859-871. 10.1080/13658810903174803.
6. Guijarro M., Pajares G., Riomoros I., Herrera P.J., Burgos-Artizzu X.P., Ribeiro A. Automatic segmentation of relevant textures in agricultural images. Comput. Electron. Agric. 2011, 75:75-83. 10.1016/j.compag.2010.09.013.
7. Hay G.J., Castilla G. Geographic object-based image analysis (GEOBIA): a new name for a new discipline. Lecture Notes in Geoinformation and Cartography 2008, 75-89. Springer, Berlin Heidelberg. T. Blaschke, S. Lang, G.J. Hay (Eds.).
8. Hay G.J., Castilla G., Wulder M.A., Ruiz J.R. An automated object-based approach for the multiscale image segmentation of forest scenes. Int. J. Appl. Earth Obs. Geoinform. 2005, 7:339-359. 10.1016/j.jag.2005.06.005.
9. Hengl T. Finding the right pixel size. Comput. Geosci. 2006, 32:1283-1298. 10.1016/j.cageo.2005.11.008.
10. Karl J.W., Maurer B.A. Spatial dependence of predictions from image segmentation: a variogram-based method to determine appropriate scales for producing land-management information. Ecol. Inform. 2010, 5:194-202. 10.1016/j.ecoinf.2010.02.004.
11. Karl J.W., Laliberte A.S., Rango A. Spatial dependence of predictions from image segmentation: a methods to determine appropriate scales for producing land-management information. Photogramm. Remote Sens. Int. Arch. 2010.
12. Kim M., Warner T.A., Madden M., Atkinson D.S. Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects. Int. J. Remote Sens. 2011, 32:2825-2850. 10.1080/01431161003745608.
13. López-Granados F. Weed detection for site-specific weed management: mapping and real-time approaches. Weed Res. 2011, 51:1-11. 10.1111/j.1365-3180.2010.00829.x.
14. Meier U. BBCH Monograph: Growth Stages for Mono- and Dicotyledonous Plants 2001, Blackwell Wiss.-Verlag, Berlin.
15. Meyer G.E., Neto J.C. Verification of color vegetation indices for automated crop imaging applications. Comput. Electron. Agric. 2008, 63:282-293. 10.1016/j.compag.2008.03.009.
16. Moffett K.B., Gorelick S.M. Distinguishing wetland vegetation and channel features with object-based image segmentation. Int. J. Remote Sens. 2013, 34:1332-1354. 10.1080/01431161.2012.718463.
17. Otsu N. A threshold selection method from gray-level histograms. IEE Trans. Syst. Man Cybern. 1979, 9:62-66.
18. Peña J.M., Torres-Sánchez J., de Castro A.I., Kelly M., López-Granados F. Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS ONE 2013, 8:e77151. 10.1371/journal.pone.0077151.
19. Tiede, D., Lang, S., Hölbling, D., Füreder, P., 2010. Transferability of OBIA rulesets for IDP camp analysis in Darfur. In: Proceedings of GEOBIA 2010-Geographic Object-Based Image Analysis. Presented at the Geographic Object-Based Image Analysis, Ghent.
20. Torres-Sánchez J., López-Granados F., De Castro A.I., Peña-Barragán J.M. Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS ONE 2013, 8:e58210. 10.1371/journal.pone.0058210.
21. Torres-Sánchez J., Peña J.M., de Castro A.I., López-Granados F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput. Electron. Agric. 2014, 103:104-113. 10.1016/j.compag.2014.02.009.
22. Woodcock C.E., Strahler A.H. The factor of scale in remote sensing. Remote Sens. Environ. 1987, 21:311-332. 10.1016/0034-4257(87)90015-0.