Abstract
Land cover is the observed physical cover on the earth’s surface. The complexity and similarity of the elements that occupy the earth’s surface make the task of interpreting aerial and satellite images very tedious, especially when it comes to very high-resolution RGB images. A neural network can be used to learn from a large amount of imagery data and is useful at classifying semantic features of imagery data. Coupled with the object-oriented approach that enriches the characteristics of the images beyond the spectral data, the abilities of ANN can be significantly improved. This paper aims to evaluate the application of ANN combined to Object-Oriented Method for the classification of a high resolution (20 cm) RGB aerial images, we took the Moulay Bouselham Region in Morocco as example. First a quad-tree then multi-resolution algorithms are used to generate homogeneous objects. Second, a set of object’s features is generated on the basis of segmentation results. Third, an Artificial neural network is tuned to rich the maximum classification performance. Finally, we discuss the results and elucidate perspective of this study.
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Acknowledgement
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 777720.
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Baroud, S., Chokri, S., Belhaous, S., Hidila, Z., Mestari, M. (2020). An Artificial Neural Network Combined to Object Oriented Method for Land Cover Classification of High Resolution RGB Remote Sensing Images. In: Hamlich, M., Bellatreche, L., Mondal, A., Ordonez, C. (eds) Smart Applications and Data Analysis. SADASC 2020. Communications in Computer and Information Science, vol 1207. Springer, Cham. https://doi.org/10.1007/978-3-030-45183-7_17
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