Abstract
The state of the art is plenty of classification methods. Pixel-based methods include the most traditional ones. Although these achieved high accuracy when classifying remote sensing images, some limits emerged with the advent of very high-resolution images that enhanced the spectral heterogeneity within a class.
Therefore, in the last decade, new classification methods capable of overcoming these limits have undergone considerable development. Within this research, we compared the performances of an Object-based and a Pixel-Based classification method, the Random Forests (RF) and the Object-Based Image Analysis (OBIA), respectively. Their ability to quantify the extension and the perimeter of the elements of each class was evaluated through some performance indices. Algorithm parameters were calibrated on a subset, then, applied on the whole image. Since these algorithms perform accurately in quantifying the elements areas, but worse if we consider the perimeters length, hence, the aim of this research was to setup some post-processing techniques to improve, in particular, this latter performance.
Algorithms were applied on peculiar classes of an area comprising the Isole dello Stagnone di Marsala oriented natural reserve, in north-western corner of Si-cily, salt pans and agricultural settlements. The area was covered by a World View-2 multispectral image consisting of eight spectral bands spanning from visible to near-infrared wavelengths and characterized by a spatial resolution of two meters. Both classification algorithms did not quantify accurately object perimeters; especially RF. Post-processing algorithm improved the estimates, which however remained more accurate for OBIA than for RF.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Liu, D., Xia, F.: Assessing object-based classification: advantages and limitations. Rem. Sens. Lett. 1(4), 187–194 (2010)
Tarantino, E., Figorito, B.: Mapping rural areas with widespread plastic covered vineyards using true color aerial data. Rem. Sens. 4(7), 1913–1928 (2012)
Koc-San, D.: Evaluation of different classification techniques for the detection of glass and plastic greenhouses from WorldView-2 satellite imagery. J. Appl. Rem. Sens. 7(1), 073553 (2013)
Tehrany, M.S., Pradhan, B., Jebuv, M.N.: A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto Int. 29(4), 351–369 (2014)
Aggarwal, N., Srivastava, M., Dutta, M.: Comparative analysis of pixel-based and object-based classification of high resolution remote sensing images – a review. Int. J. Eng. Trends Technol. (IJETT) 38(1), 5–11 (2016)
Cai, L., Shi, W., Miao, Z., Hao, M.: Accuracy assessment measures for object extraction from remote sensing images. Rem. Sens 10(2), 303 (2108)
Tomas, I.L.: Spatial postprocessing of spectrally classified Landsat data. Photogram. Eng. Rem. Sens. 46, 1201–1206 (1980)
Townshend, J.R.G.: The enhancement of computer classification by logical smoothing. Photogram. Eng. Rem. Sens. 52, 213–221 (1986)
Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M.: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogram. Rem. Sens. 58, 239–258 (2004)
Jensen, J.R., Qiu, F., Patterson, K.: A neural network image interpretation system to extract rural and urban land use and land cover information from remote sensor data. Geocarto Int. 16(1), 1–10 (2001)
Weibel, R.: Generalization of spatial data: principles and selected algorithms. In: van Kreveld, M., Nievergelt, J., Roos, T., Widmayer, P. (eds.) CISM School 1996. LNCS, vol. 1340, pp. 99–152. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-63818-0_5
Ciraolo, G., Cox, E., La Loggia, G., Maltese, A.: The classification of submerged vegetation using hyperspectral MIVIS data. Ann. Geophys. 49(1), 287–294 (2006)
Karpouzli, E., Malthus, T.: The empirical line method for the atmospheric correction of IKONOS imagery. Int. J. Rem. Sens. 24, 1143–1150 (2003)
Pipitone, C., Maltese, A., Dardanelli, G., Capodici, F., Lo Brutto, M., La Loggia, G.: Detection of a reservoir water level using shape similarity metrics. In: Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX, Proceedings, vol. 10421, p. 104211L (2017)
Duro, D., Franklin, E., Dubé, G.: A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Rem. Sens. Environ. 1(18), 259–272 (2012)
Breiman, L.: Random Forests. Mach. Learn. 45, 5–32 (2001)
Böhner, J., Selige, T., Ringeler, A.: Image segmentation using representativeness analysis and region growing. Göttinger. Geogr. Abh. 115, 29–38 (2006)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. In: Proceedings of the First International Conference on Computer Vision, pp. 259–268. IEEE Computer Society Press (1987)
Burghardt, D.: Glättung mit Snakes. Festschrift zum 65. Geburtstag von Prof. Dr. Ing. habil. Siegfried Meier. TU Dresden (2002)
Acknowledgments
The authors would like to thank G. Ciraolo for helping in collecting spectroradiometric data and for his technical advices.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sarzana, T., Maltese, A., Capolupo, A., Tarantino, E. (2020). Post-processing of Pixel and Object-Based Land Cover Classifications of Very High Spatial Resolution Images. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12252. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_57
Download citation
DOI: https://doi.org/10.1007/978-3-030-58811-3_57
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58810-6
Online ISBN: 978-3-030-58811-3
eBook Packages: Computer ScienceComputer Science (R0)