Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania)

Abstract : The use of geographic knowledge in remote sensing constitutes one of the fundamental base of the methodologies of image processing. Image processing, image analysis, and oriented-object recognition are based on the geographic knowledge. More specifically, the large panel of supervised classifications methods are one of the main example where geographic knowledge is necessary for both algorithms training and results validation. Recently, with the coming back of the artificial intelligence (AI) wave, it appears that a large spectrum of usually employed methodologies in remote sensing and image processing, are one of the main drivers of AI: machine learning, deep learning are the most effective’s examples. As well as many based processing algorithms like the Support Vector Machine (SVM) or the Random Forest (RF). However, despite the constant performances of the methods of calculus; the geographic knowledge’s determines the accuracy of recognition and classification in image processing and spatial modelling generated. In regard of the fast seasonal and annual landscape changes in the Arctic climate, and complex urban structures, Yakutsk and Kaunas cities contribute to the reflexion.
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Submitted on : Sunday, May 5, 2019 - 10:52:53 AM
Last modification on : Saturday, November 30, 2019 - 12:34:07 PM

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Sébastien Gadal, Walid Ouerghemmi. Knowledge Models and Image Processing Analysis in Remote Sensing: Examples of Yakutsk (Russia) and Kaunas (Lithuania). 5th International Conference on Geographical Information Systems Theory, Applications and Management, INSTICC; University of Crete; IEEE GRSS; ACM SIGSPATIAL, May 2019, Heraklion, Greece. pp.282-288, ⟨10.5220/0007752202820288⟩. ⟨hal-02120100⟩

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