A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images
This work addresses computational challenges in hyperspectral image analysis for remote sensing applications, but it is incremental as it builds on existing filter methods with a novel band selection strategy.
The authors tackled the problem of high dimensionality in hyperspectral image processing by proposing a new filter approach based on information gain for dimensionality reduction and classification, which outperformed three competing methods on benchmark datasets by reducing computational cost and improving classification accuracy.
Recently, the hyperspectral sensors have improved our ability to monitor the earth surface with high spectral resolution. However, the high dimensionality of spectral data brings challenges for the image processing. Consequently, the dimensionality reduction is a necessary step in order to reduce the computational complexity and increase the classification accuracy. In this paper, we propose a new filter approach based on information gain for dimensionality reduction and classification of hyperspectral images. A special strategy based on hyperspectral bands selection is adopted to pick the most informative bands and discard the irrelevant and noisy ones. The algorithm evaluates the relevancy of the bands based on the information gain function with the support vector machine classifier. The proposed method is compared using two benchmark hyperspectral datasets (Indiana, Pavia) with three competing methods. The comparison results showed that the information gain filter approach outperforms the other methods on the tested datasets and could significantly reduce the computation cost while improving the classification accuracy. Keywords: Hyperspectral images; dimensionality reduction; information gain; classification accuracy. Keywords: Hyperspectral images; dimensionality reduction; information gain; classification accuracy.