IEEE Trans Image Process - Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach.

Tópicos

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Resumo

In this paper, a new method for supervised hyperspectral data classification is proposed. In particular, the notion of stochastic minimum spanning forest (MSF) is introduced. For a given hyperspectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of MSFs. The next step consists in building an MSF from each of the M marker maps. Finally, all the M realizations are aggregated with a maximum vote decision rule in order to build the final classification map. The proposed method is tested on three different data sets of hyperspectral airborne images with different resolutions and contexts. The influences of the number of markers and of the number of realizations M on the results are investigated in experiments. The performance of the proposed method is compared to several classification techniques (both pixelwise and spectral-spatial) using standard quantitative criteria and visual qualitative evaluation.

Resumo Limpo

paper new method supervis hyperspectr data classif propos particular notion stochast minimum span forest msf introduc given hyperspectr imag pixelwis classif first perform classif map m marker map generat random select pixel label marker construct msfs next step consist build msf m marker map final m realiz aggreg maximum vote decis rule order build final classif map propos method test three differ data set hyperspectr airborn imag differ resolut context influenc number marker number realiz m result investig experi perform propos method compar sever classif techniqu pixelwis spectralspati use standard quantit criteria visual qualit evalu

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