Микола Сергійович Лавренюк


Obtaining reliable and accurate crop classification and land cover map based on satellite data, in particular high resolution data, is one of the most important tasks in remote sensing. Such maps provide basic information for many other applied problems and are vital in remote sensing studies. Despite of which machine learning methods were utilized for maps obtaining: traditional (Random Forest, Support Vector Machine, Multi-layer perceptron, logistic regression) or state-of-the-art approaches (autoencoder, convolutional neural network, recurrent neural network) there is some noise (single pixels or groups and clusters of pixels that wrong classify) on such maps. There are traditional methods for noise reduction, however these methods do not take into account image semantics. Therefore, they are not effective for filtration land cover and crop classification maps based on satellite images. The most complicated task in the filtering such maps is to preserve edges and boundaries between different agricultural fields. Often these boundaries are small and common filters consider them like a noise and remove them. Therefore, final crop classification map after filtration using common methods is smoothed and all edges are loosed. Thus, in this paper we proposed new method for boundaries identification on the crop classification map based on modified Sobel algorithm. It is impossible to use gradient based methods for boundaries detection because important peculiarity of the crop classification map that it has finite discrete set of pixel values. We proposed modification of Sobel algorithm based on using additional steps of processing. These steps consist of convolution with structural element (square), threshold filter (considers all objects that have square less than threshold as a noise and remove them) and morphological closing operation for boundaries detection between agricultural fields but not for other changes in pixel values identification. Accuracy and efficiency of this method with the proposed filtration method have been tested on the independent set and using the visual comparison with the results of utilizing common filters.


crop classification; postprocessing; filtration; edge detection; Sobel algorithm


Huang, Xin, et al. New postprocessing methods for remote sensing image classification: A systematic study. IEEE Transactions on Geoscience and Remote Sensing, vol. 52, iss. 11, 2014, pp. 7140-7159.

Kolotii, A., et al. Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 40, iss.7, 2015, pp. 39-44.

Gallego, Francisco Javier, et al. Efficiency assessment of using satellite data for crop area estimation in Ukraine. International Journal of Applied Earth Observation and Geoinformation, vol. 29, 2014, pp. 22-30.

Kogan, Felix, et al. Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models. International Journal of Applied Earth Observation and Geoinformation, vol. 23, 2013, pp. 192-203.

Kussul, Nataliia, et al. Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters, vol. 14, iss. 5, 2017, pp. 778-782.

Shelestov, Andrii, et al. Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Frontiers in Earth Science, vol. 5, 2017, pp. 17.

Kussul, Nataliia, et al. Parcel-based crop classification in ukraine using landsat-8 data and sentinel-1A data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, iss. 6, 2016, pp. 2500-2508.

Waldner, François, et al. Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity. International Journal of Remote Sensing, vol. 37, iss. 14, 2016, pp. 3196-3231.

Townsend, F. E. The enhancement of computer classifications by logical smoothing. Photogrammetric Engineering and Remote Sensing, vol. 52, iss. 2, 1986, pp. 213-221.

Kim, Kwang E. Adaptive majority filtering for contextual classification of remote sensing data." International Journal of Remote Sensing, vol. 17, iss. 5, 1996, pp. 1083-1087.

Löw, Fabian, Christopher Conrad, and Ulrich Michel. Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 108, 2015, pp. 191-204.

Haralick, Robert M., Stanley R. Sternberg, and Xinhua Zhuang. Image analysis using mathematical morphology. IEEE transactions on pattern analysis and machine intelligence, vol. 4, 1987, pp. 532-550.

Kupidura, Przemysław, and Magdalena Jakubiak. The morphological filtering of the remote sensing images for the noise reduction comparing to traditional filters. Roczniki Geomatyki, vol. 7, iss. 2, 2009, pp. 63-68.

Jensen, John R., Fang Qiu, and Keith Patterson. A neural network image interpretation system to extract rural and urban land use and land cover information from remote sensor data. Geocarto International, vol. 16, iss. 1, 2001, pp. 21-30.

Qian, Yu, Kang Zhang, and Fang Qiu. Spatial contextual noise removal for post classification smoothing of remotely sensed images. Proceedings of the 2005 ACM symposium on Applied computing. ACM, Santa Fe, New Mexico, March 13 - 17, 2005, pp.524-528.

LUCAS 2009, Land Use / Cover Area Frame Survey. Available at: (аccessed 12.06.2017).

Global Land Cover 30. Available at: (аccessed 12.06.2017).

CORINE Land Cover. Available at: (аccessed 12.06.2017).

Large-scale classification of land cover using retrospective satellite data. Cybernetics and Systems Analysis, vol. 52, iss. 1, 2016, pp. 127-138.

Lavrenyuk, Mykola. Metod ob"yektnoyi fil'tratsiyi kart klasyfikatsiyi zemnoho pokryvu na osnovi morfolohichnykh oznak [Method of object filtering maps of the classification of the earth's surface on the basis of morphological features]. 2017. (unpublished).

Peli, Tamar, and David Malah. A study of edge detection algorithms. Computer graphics and image processing, vol. 20, iss. 1, 1982, pp. 1-21.

Congalton, Russell G. A review of assessing the accuracy of classifications of remotely sensed data. Remote sensing of environment, vol. 37, iss. 1, 1991, pp. 35-46.

Foody, Giles M. Classification accuracy comparison: hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority. Remote Sensing of Environment, vol. 113, iss. 8, 2009, pp. 1658-1663.

Foody, Giles M. Thematic map comparison. Photogrammetric Engineering & Remote Sensing, vol. 70, iss. 5, 2004, pp. 627-633.

Skakun, Sergii, et al. Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, iss. 8, 2016, pp. 3712-3719.

Kussul, Nataliia, et al. Regional scale crop mapping using multi-temporal satellite imagery. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 40, iss. 7, 2015, pp. 45–52. DOI:10.5194/isprsarchives-XL-7-W3-45-2015.


  • There are currently no refbacks.