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Robust Statistics for Classification of Remote Sensing Data

Abstract : The classification of remote sensing data from Landsat 7 satellite is considered, and an area under investigation is Jakarta Province. The supervised land classification is done with two processes: the training sites and classification process. A robust computationally efficient approach is applied for training site to deal with the large remote sensing data set of Jakarta. The objective of this paper is to introduce the depth function for robust estimation of a multivariate location parameter minimizing vector variance for classification of green space at Jakarta Province.
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Submitted on : Monday, March 30, 2020 - 7:09:13 PM
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Dyah E. Herwindiati, Maman A. Djauhari, Luan Jaupi. Robust Statistics for Classification of Remote Sensing Data. 20th International Conference on Computational Statistics. COMPSTAT 2012, Aug 2012, Limassol, Cyprus. pp.317-328. ⟨hal-02468060⟩



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