Hyperspectral dimension reduction using global and local information based linear discriminant analysis
Hyperspectral image classification has become an important research topic in remote sensing. Because of high dimensional data, a special attention is needed dealing with spectral data; and thus, one of the research topics in hyperspectral image classification is dimension reduction. In this paper, a dimension reduction approach is presented for classification on hyperspectral images. Advantages of the usage of not only global pattern information, but also local pattern information are examined in hyperspectral image processing. In addition, not only tuning the parameters, but also an experimental analysis of the distribution of the hyperspectral data is demonstrated. Therefore, how global or local pattern variations play an important role in classification is examined. According to the experimental outcomes, the promising results are obtained for classification on hyperspectral images.