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[HDJ13] Robust Classification of Remote Sensing Data for Green Space Analysis
Revue Internationale avec comité de lecture : Journal Journal of Mathematics and System Science, vol. 3(4), pp. 180-186, 2013, (doi:JMSS E20130319-01)Mots clés: Data depth, minimum vector variance, Mahalanobis distance, remote sensing, robust
Résumé:
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 Jakarta.
Equipe:
msdma
BibTeX
| @article { | |||
| HDJ13, | |||
| title | = | "{Robust Classification of Remote Sensing Data for Green Space Analysis}", | |
| author | = | "D. Herwindiati and M. Djauhari and L. Jaupi", | |
| journal | = | "Journal of Mathematics and System Science", | |
| year | = | 2013, | |
| volume | = | 3, | |
| number | = | 4, | |
| pages | = | "180-186", | |
| doi | = | "JMSS E20130319-01", | |
| } | |||
