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A comparative analysis of classification methods for hyperspectral images generated with conventional dimension reduction methods
2017
Turkish Journal of Electrical Engineering and Computer Sciences
This paper compared performances of classification methods for a hyperspectral image dataset in view of dimensionality reduction (DR). Among conventional DR methods, principal component analysis, maximum noise fraction, and independent component analysis were used for the purpose of dimension reduction. The study was conducted using these DR techniques on a real hyperspectral image, an AVIRIS dataset with 224 bands, throughout the experiments. It was observed that DR may have a significant
doi:10.3906/elk-1503-167
fatcat:7wprqkv465dshku3hekwbccrhe