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Assessment of spectral reduction techniques for endmember extraction in unmixing of hyperspectral images

George, EB and Ternikar, CR and Ghosh, R and Nagesh Kumar, D and Gomez, C and Ahmad, T and Sahadevan, AS and Gupta, PK and Misra, A (2022) Assessment of spectral reduction techniques for endmember extraction in unmixing of hyperspectral images. In: Advances in Space Research .

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Official URL: https://doi.org/10.1016/j.asr.2022.06.028


Spectral mixture modelling is one of the most important techniques for classifying hyperspectral data at sub-pixel resolution. The identification of spectrally pure endmembers for estimating their corresponding abundances is an important step in spectral unmixing. The application of spectral reduction prior to endmember extraction would optimize the process by increasing the sensitivity of the algorithms to the most distinctive and informative features of the dataset. The objective of this study is to compare different spectral reduction techniques prior to endmember extraction on six real hyperspectral datasets, including an Airborne Visible InfraRed Imaging Spectrometer- Next Generation (AVIRIS-NG) image over Indian sub-continent. The endmembers identified from different combinations of spectral reduction and endmember extraction techniques are used for linear spectral unmixing on the original datasets. The performance of such combinations after unmixing were compared in terms of pixel reconstruction error and also the computation time for each dataset. Spectral reduction by both feature extraction techniques like Principal Component Analysis (PCA), Independent Component Analysis (ICA), Minimum Noise Fraction (MNF), and a feature selection technique based on Partial Informational Correlation (PIC) measure were analysed. The PIC based spectral reduction was found to perform well in terms of reconstruction error and computation time when combined with N-FINDR endmember algorithm. This approach could be adopted for spectral reduction in unmixing of datasets with similar endmember classes.

Item Type: Journal Article
Publication: Advances in Space Research
Publisher: Elsevier Ltd
Additional Information: The copyright for this article belongs to the Elsevier Ltd.
Keywords: Errors; Extraction; Feature extraction; Hyperspectral imaging; Pixels; Principal component analysis; Thermography (imaging), Airborne visible infrared imaging spectrometer- next generation; Airborne Visible Infrared/Imaging Spectrometers; Endmember extraction; Endmembers; Information correlation; Partial information; Partial information correlation; Reconstruction error; Spectral reductions; Spectral unmixing, Independent component analysis
Department/Centre: Division of Mechanical Sciences > Divecha Centre for Climate Change
Division of Interdisciplinary Sciences > Interdisciplinary Centre for Water Research
Division of Mechanical Sciences > Civil Engineering
Date Deposited: 28 Jul 2022 05:23
Last Modified: 28 Jul 2022 05:23
URI: https://eprints.iisc.ac.in/id/eprint/75016

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