A new band selection approach based on information theory and support vector machine for hyperspectral images reduction and classification
This work addresses computational challenges in hyperspectral image processing for remote sensing or similar domains, but it appears incremental as it builds on existing mutual information methods.
The paper tackled the problem of high dimensionality in hyperspectral images by proposing a new band selection strategy based on joint mutual information to improve computational speed and classification accuracy, showing that it outperforms reproduced filters on the AVIRIS 92AV3C dataset using SVM.
The high dimensionality of hyperspectral images consisting of several bands often imposes a big computational challenge for image processing. Therefore, spectral band selection is an essential step for removing the irrelevant, noisy and redundant bands. Consequently increasing the classification accuracy. However, identification of useful bands from hundreds or even thousands of related bands is a nontrivial task. This paper aims at identifying a small set of highly discriminative bands, for improving computational speed and prediction accuracy. Hence, we proposed a new strategy based on joint mutual information to measure the statistical dependence and correlation between the selected bands and evaluate the relative utility of each one to classification. The proposed filter approach is compared to an effective reproduced filters based on mutual information. Simulations results on the hyperpectral image HSI AVIRIS 92AV3C using the SVM classifier have shown that the effective proposed algorithm outperforms the reproduced filters strategy performance. Keywords-Hyperspectral images, Classification, band Selection, Joint Mutual Information, dimensionality reduction ,correlation, SVM.