Hyperspectral Data Dimensionality Reduction Using Hybrid Approach
نویسندگان
چکیده
Hyperspectral data contain a large volume of information. This abundance of data is hard to exploit due to high computational cost involved in processing this data. Dimensionality reduction deals with transforming high dimensional data in to lower dimensional space without losing significance of the High dimensional data. In this paper, a new methodology has been proposed that is based on existing algorithms. This model uses the advantages of PCA, ICA, and MNF to reduce dimensionality with high classification accuracy. Traditional AVIRIS Indian Pine benchmark dataset has been used for experiment. ENVI is used for dimension reduction. ERDAS Imagine has been used for Classification using Maximum likelihood method. Keywords— Hyperspectral, Dimensionality reduction, AVIRIS, ENVI, ERDAS imagine
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