Hybridization of filter and wrapper approaches for the dimensionality reduction and classification of hyperspectral images
This work addresses computational efficiency and classification accuracy for hyperspectral image processing, representing an incremental improvement over existing methods.
The paper tackled the problem of high dimensionality in hyperspectral images by proposing a hybrid band selection algorithm combining mutual information gain, mRMR, and SVM-PF, which outperformed a reproduced filters approach on the AVIRIS 92AV3C dataset.
The high dimensionality of hyperspectral images often imposes a heavy computational burden for image processing. Therefore, dimensionality reduction is often an essential step in order to remove the irrelevant, noisy and redundant bands. And consequently, increase 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 bands, for improving computational speed and prediction accuracy. Hence, we have proposed a hybrid algorithm through band selection for dimensionality reduction of hyperspectral images. The proposed approach combines mutual information gain (MIG), Minimum Redundancy Maximum Relevance (mRMR) and Error probability of Fano with Support Vector Machine Bands Elimination (SVM-PF). The proposed approach is compared to an effective reproduced filters approach based on mutual information. Experimental results on HSI AVIRIS 92AV3C have shown that the proposed approach outperforms the reproduced filters. Keywords - Hyperspectral images, Classification, band Selection, filter, wrapper, mutual information, information gain.