Categorization based on sparse coding for SAR patch categorization

Konferenz: EUSAR 2016 - 11th European Conference on Synthetic Aperture Radar
06.06.2016 - 09.06.2016 in Hamburg, Germany

Tagungsband: EUSAR 2016

Seiten: 4Sprache: EnglischTyp: PDF

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Gleich, Dusan; Sipos, Danijel (University of Maribor, Faculty of Electrical Engineering and computer Science, Slovenia)

This paper presents SAR patch categorization. The novelty of this paper is an optimal dual based sparse coding, applied to SAR patch categorization. The motivation of this paper was to achieve better performances by using dual based L1 analysis than standard L1 analysis. The sparse categorization is implemented within sparse framework and compared with the bag of visual words categorization. Experimental results showed that sparse classification requires a large database for learning in order to achieve the same performance results as BoW.