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The Influence of Noise Intensity in the Nonlinear Spectral Unmixing of Hyperspectral Data

Der Einfluss der Rauschintensität bei der nichtlinearen spektralen Entmischung hyperspektraler Daten

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Abstract

Noise effect as an unwanted and troubler component was investigated in this study. It generally exists more or less in the remote sensing data because of the device errors and natural effects. Therefore, its correct estimation will lead to better analysis. This paper aims to examine the noise effect on selecting the spectral mixing model. A set of synthetic data was first designed based on one linear and five nonlinear models. Then, the noise was added to the data at different signal-to-noise (SNR) levels. After designing the models, to evaluate the noise intensity, it was determined using the noise estimation methods (multiple linear regression (MLR) based method and L1HyMixDe), assuming that each synthetic dataset stayed on the linear model. A comparison was made between the obtained noise values from the linear and each of the nonlinear models using one-way Analysis of Variance (ANOVA) and Wilcoxon statistical tests. According to the significant difference between the noise values of linear and nonlinear data in different SNR levels, an SNR limit was determined for each model and below this value, the noise overcomes the nonlinear portion of the data. As a result, Polynomial Post Nonlinear Mixing Model (PPNMM) shows the best performance in the nonlinear unmixing of data in the presence of noise. This study was tested on real Hyperion data and the obtained results agreed with our assessments.

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Authors and Affiliations

  1. Mining Engineering Faculty, Sahand University of Technology, Tabriz, Iran

    Hadi Jamshid Moghadam & Majid Mohammady Oskouei

  2. Faculty of Engineering, University of Mohaghegh Ardabili, Ardabil, Iran

    Tohid Nouri

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  1. Hadi Jamshid Moghadam
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  2. Majid Mohammady Oskouei
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  3. Tohid Nouri
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Correspondence to Majid Mohammady Oskouei.

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Jamshid Moghadam, H., Mohammady Oskouei, M. & Nouri, T. The Influence of Noise Intensity in the Nonlinear Spectral Unmixing of Hyperspectral Data. PFG 91, 29–42 (2023). https://doi.org/10.1007/s41064-022-00223-x

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  • DOI: https://doi.org/10.1007/s41064-022-00223-x

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