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Improved Subspace Detection Based on Minimum Noise Fraction and Mutual Information for Hyperspectral Image Classification

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Proceedings of International Joint Conference on Computational Intelligence

Abstract

Finding an informative subspace of the original in hyperspectral images has become very essential due to its comprehensive applications in ground object recognition. Information extraction from hyperspectral images is a challenging work on account of its high correlation among the image bands in both the spatial and spectral redundancy. A feature reduction approach combining both the feature extraction and feature selection is proposed in this paper. A combination of Minimum Noise Fraction (MNF) and Mutual Information (MI) is proposed to select the subspace of the original datacube with regard to achieve improved classification accuracy. In the proposed method, feature ranking is improved by scaling the mutual information to a specific range in order to avoid redundant features. The proposed technique (MNF-nMI) is tested on two hyperspectral images captured by NASA AVIRIS sensor and HYDICE sensor. The experimental results typically indicate the noticeable improvement pertaining to classification accuracy. The proposed technique shows the classification accuracy of 96.8% and 99.3% on AVIRIS and HYDICE hyperspectral data respectively which is greater than the conventional methods studied.

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Correspondence to Md. Rashedul Islam .

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Rashedul Islam, M., Ali Hossain, M., Ahmed, B. (2020). Improved Subspace Detection Based on Minimum Noise Fraction and Mutual Information for Hyperspectral Image Classification. In: Uddin, M., Bansal, J. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_53

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