Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

International Journal of Applied Earth Observation and Geoinformation

Volumn 52, Issue , 2016, Pages 403-411

  Novelli, Antonio ^a   Aguilar, Manuel A ^b   Nemmaoui, Abderrahim ^b   Aguilar, Fernando J ^b   Tarantino, Eufemia ^a  

^a POLITECNICO DI BARI   (Italy)

^b UNIVERSITY OF ALMERÍA   (Spain)

Author keywords

Greenhouses; Landsat8 OLI; Object based classification; Segmentation quality; Sentinel 2 MSI; WorldView 2

Indexed keywords

CLASSIFICATION; GREENHOUSE GAS; IMAGE ANALYSIS; LANDSAT; SEGMENTATION; SENTINEL; WORLDVIEW;

ALMERIA [ANDALUCIA]; ANDALUCIA; SPAIN;

EUCLIDIA;

EID: 84998673750     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/j.jag.2016.07.011     Document Type: Article

References (47)

1. Agüera, F., Aguilar, M., Aguilar, F., Detecting greenhouse changes from QB imagery on the Mediterranean Coast. Int. J. Remote Sens., 27, 2006, 4751-4767.
2. Agüera, F., Aguilar, F.J., Aguilar, M.A., Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses. ISPRS J. Photogramm. Remote Sens. 63 (2008), 635––646.
3. Aguilar, M.A., Bianconi, F., Aguilar, F.J., Fernández, I., Object-based greenhouse classification from GeoEye-1 and WorldView-2 stereo imagery. Remote Sens. 6 (2014), 3554–3582.
4. Aguilar, M.A., Vallario, A., Aguilar, F.J., Lorca, A.G., Parente, C., Object-based greenhouse horticultural crop identification from multi-temporal satellite imagery: a case study in Almeria, Spain. Remote Sens. 7 (2015), 7378–7401.
5. Arcidiacono, C., Porto, S., Pixel-based classification of high-resolution satellite images for crop-shelter coverage recognition. XXVIII International Horticultural Congress on Science and Horticulture for People (IHC2010): International Symposium on 937, 2010, 1003–1010.
6. Baatz, M., Schäpe, A., Multiresolution Segmentation: an Optimization Approach for High Quality Multi-scale Image Segmentation. 2000, Herbert Wichmann Verlag, Berlin, Germany, 12–23.
7. Berk, A., Bernstein, L., Anderson, G., Acharya, P., Robertson, D., Chetwynd, J., Adler-Golden, S., MODTRAN cloud and multiple scattering upgrades with application to AVIRIS. Remote Sens. Environ. 65 (1998), 367–375.
8. Breiman, L., Random forests. Mach. Learn. 45 (2001), 5––32.
9. Briassoulis, D., Babou, E., Hiskakis, M., Scarascia, G., Picuno, P., Guarde, D., Dejean, C., Review, mapping and analysis of the agricultural plastic waste generation and consolidation in Europe. Waste Manage. Res. 31 (2013), 1262–1278.
10. Carvajal, F., Agüera, F., Aguilar, F., Aguilar, M., Relationship between atmospheric corrections and training-site strategy with respect to accuracy of greenhouse detection process from very high resolution imagery. Int. J. Remote Sens. 31 (2010), 2977–2994.
11. Chen, Z., Wang, L., Wu, W., Jiang, Z., Li, H., Monitoring plastic-mulched farmland by Landsat-8 OLI imagery using spectral and textural features. Remote Sens., 8, 2016, 353.
12. Clinton, N., Holt, A., Scarborough, J., Yan, L., Gong, P., Accuracy assessment measures for object-based image segmentation goodness. Photogramm. Eng. Remote Sens. 76 (2010), 289–299.
13. Congalton, R.G., Green, K., Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2008, CRC Press.
14. Congalton, R.G., A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37 (1991), 35–46.
15. Darwish, A., Leukert, K., Reinhardt, W., Image segmentation for the purpose of object-based classification. Int. Geosci. Remote Sens. Symp. III (2003), 2039–2041.
16. Definiens, A., Definiens eCognition Developer 8 Reference Book. 2009, Definiens AG, München.
17. Dietterich, T.G., An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach. Learn. 40 (2000), 139–157.
18. Drăguţ, L., Csillik, O., Eisank, C., Tiede, D., Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS J. Photogramm. Remote Sens. 88 (2014), 119–127.
19. Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., Li, X., Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sens., 8, 2016, 354.
20. Fernández-Manso, A., Fernández-Manso, O., Quintano, C., SENTINEL-2A red-edge spectral indices suitability for discriminating burn severity. Int. J. Appl. Earth Obs. Geoinf. 50 (2016), 170–175.
21. Frauman, E., Wolff, E., Segmentation of very high spatial resolution satellite images in urban areas for segments-based classification. Proceedings for 3rd International Symposium Remote Sensing and Data Fusion Over Urban Areas, Tempe, Arizona, 2005.
22. Gitelson, A.A., Kaufman, Y.J., Stark, R., Rundquist, D., Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 80 (2002), 76–87.
23. Haralick, R.M., Shanmugam, K., Dinstein, I.H., Textural features for image classification. IEEE Trans. Syst. Man Cybern.: Syst., 1973, 610–621.
24. Hill, T., Lewicki, P., STATISTICS Methods and Applications. 2007, StatSoft, Tulsa, USA.
25. Immitzer, M., Vuolo, F., Atzberger, C., First experience with Sentinel-2 data for crop and tree species classifications in Central Europe. Remote Sens., 8, 2016, 166.
26. Koc-San, D., Evaluation of different classification techniques for the detection of glass and plastic greenhouses from WorldView-2 satellite imagery. J. Appl. Remote Sens., 7, 2013, 073553.
27. Landis, J.R., Koch, G.G., The measurement of observer agreement for categorical data. Biometrics, 1977, 159–174.
28. Liu, D., Xia, F., Assessing object-based classification: advantages and limitations. Remote Sens. Lett. 1 (2010), 187–194.
29. Liu, Y., Bian, L., Meng, Y., Wang, H., Zhang, S., Yang, Y., Shao, X., Wang, B., Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis. ISPRS J. Photogramm. Remote Sens. 68 (2012), 144–156.
30. Lu, L., Di, L., Ye, Y., A Decision-tree classifier for extracting transparent plastic-mulched landcover from Landsat-5 TM images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7 (2014), 4548–4558.
31. Lu, L., Hang, D., Di, L., Threshold model for detecting transparent plastic-mulched landcover using moderate-resolution imaging spectroradiometer time series data: a case study in southern Xinjiang, China. J. Appl. Remote Sens., 9, 2015, 097094.
32. Muller-Wilm, U., Louis, J., Richter, R., Gascon, F., Niezette, M., Sentinel-2 Level 2A Prototype Processor: Architecture, Algorithms And First Results. 2013, ESA Special Publication, p98.
33. Novelli, A., Tarantino, E., Combining ad hoc spectral indices based on LANDSAT-8 OLI/TIRS sensor data for the detection of plastic cover vineyard. Remote Sens. Lett., 2015.
34. Novelli, A., Tarantino, E., 2015. The contribution of Landsat 8 TIRS sensor data to the identification of plastic covered vineyards, 2015, pp. 95351E-95351E-95359.
35. Pala, E., Tasdemir, K., Koc-San, D., Unsupervised extraction of greenhouses using approximate spectral clustering ensemble. Geosci. Remote Sens. Symp. (IGARSS), IEEE Int. IEEE, 2015, 4668–4671.
36. Pesaresi, M., Corbane, C., Julea, A., Florczyk, A.J., Syrris, V., Soille, P., Assessment of the added-value of Sentinel-2 for detecting built-up areas. Remote Sens., 8, 2016, 299.
37. Picuno, P., Tortora, A., Capobianco, R.L., Analysis of plasticulture landscapes in Southern Italy through remote sensing and solid modelling techniques. Landscape Urban Plann. 100 (2011), 45–56.
38. Rodriguez-Galiano, V.F., Ghimire, B., Rogan, J., Chica-Olmo, M., Rigol-Sanchez, J.P., An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogramm. Remote Sens. 67 (2012), 93–104.
39. Rouse, J.W. Jr, Haas, R., Schell, J., Deering, D., Monitoring Vegetation Systems in the Great Plains with ERTS, 351, 1974, NASA Special Publication, 309.
40. Roy, D.P., Wulder, M., Loveland, T., Woodcock, C., Allen, R., Anderson, M., Helder, D., Irons, J., Johnson, D., Kennedy, R., Landsat-8: science and product vision for terrestrial global change research. Remote Sens. Environ. 145 (2014), 154–172.
41. Smith, A., Image segmentation scale parameter optimization and land cover classification using the random forest algorithm. J. Spat. Sci. 55 (2010), 69–79.
42. Tarantino, E., Figorito, B., Mapping rural areas with widespread plastic covered vineyards using true color aerial data. Remote Sens. 4 (2012), 1913–1928.
43. Tian, J., Chen, D.M., Optimization in multi‐scale segmentation of high‐resolution satellite images for artificial feature recognition. Int. J. Remote Sens. 28 (2007), 4625–4644.
44. Townshend, J.R., Justice, C.O., Gurney, C., McManus, J., The impact of misregistration on change detection. IEEE Trans. Geosci. Remote Sens. 30 (1992), 1054–1060.
45. Wu, C.F., Deng, J.S., Wang, K., Ma, L.G., Tahmassebi, A.R.S., Object-based classification approach for greenhouse mapping using Landsat-8 imagery. Int. J. Agric. Biol. Eng., 9, 2016, 79.
46. Yang, J., He, Y., Caspersen, J., Jones, T., A discrepancy measure for segmentation evaluation from the perspective of object recognition. ISPRS J. Photogramm. Remote Sens. 101 (2015), 186–192.
47. Zhang, J., Pu, R., Yuan, L., Wang, J., Huang, W., Yang, G., Monitoring powdery mildew of winter wheat by using moderate resolution multi-temporal satellite imagery. PLoS One, 9, 2014, e93107.