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

The Influence of Noise Intensity in the Nonlinear Spectral Unmixing of Hyperspectral Data 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science Springer Journals

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

Jamshid Moghadam, Hadi; Mohammady Oskouei, Majid; Nouri, Tohid
PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science , Volume 91 (1) – Mar 1, 2023
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Publisher
Springer Journals
Copyright
Copyright © Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
2512-2789
eISSN
2512-2819
DOI
10.1007/s41064-022-00223-x
Publisher site
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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.

Journal

PFG – Journal of Photogrammetry Remote Sensing and Geoinformation Science – Springer Journals

Published: Mar 1, 2023

Keywords: Noise; Nonlinear model; Unmixing; SNR; ANOVA; Wilcoxon test

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