Noise estimation of hyperspectral remote sensing image based on multiple linear regression and wavelet transform

Xu, Dianxiang; Sun, Lei; Luo, Jianshu

doi:10.1590/s1982-21702013000400008

Cited by 8 publications

(3 citation statements)

References 19 publications

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“…Since the imaging spectrometer takes a long time to collect the complete scanning images, electromagnetic radiation in the external space causes complicated impacts on the hyperspectral imaging pathway and brings abundant noise interferences to sample images of some wavebands (Xu et al 2013)…”

Section: High-noise Bands Removing Based On Lstmmentioning

confidence: 99%

Maize Haploid Identification via LSTM-CNN and Hyperspectral Imaging Technology

Wang¹,

Liao²,

An³

et al. 2018

Preprint

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Accurate and fast identification of seed cultivars is crucial to plant breeding, with accelerating breeding of new products and increasing its quality. In our study, the first attempt to design a high-accurate identification model of maize haploid seeds from diploid ones based on optimum waveband selection of the LSTM-CNN algorithm is realized via deep learning and hyperspectral imaging technology, with accuracy reaching 97% in the determining optimum waveband of 1367.6-1526.4nm. The verification of testing another cultivar achieved an accuracy of 93% in the same waveband. The model collected images of 256 wavebands of seeds in the spectral region of 862.9-1704.2nm. The high-noise waveband intervals were found and deleted by the LSTM. The optimum-data waveband intervals were determined by CNN's waveband-based detection. The optimum sample set for network training only accounted for 1/5 of total sample data. The accuracy was significantly higher than the full-waveband modeling or modeling of any other wavebands. Our study demonstrates that the proposed model has outstanding effect on maize haploid identification and it could be generalized to some extent.

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Section: High-noise Bands Removing Based On Lstmmentioning

confidence: 99%

Maize Haploid Identification via LSTM-CNN and Hyperspectral Imaging Technology

Wang¹,

Liao²,

An³

et al. 2018

Preprint

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“…Under this hypothesis, the photon noise is supposed to be negligible. Therefore, in previous work [8][9][10][11][12], the noise model is based on the SI-additive Gaussian white noise. In this model, the noise variances vary with the wavelength, while the noise variance of each band is constant.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the above spatial decorrelation in homogeneous regions, Xu et al, estimate the variance of the noise in the wavelet domain. The above noise estimation methods achieve good performance for the SI noise, but are not able to estimate the variances of the SD (signal-dependent) noise [12].…”

Section: Introductionmentioning

confidence: 99%

Superpixel-Based Mixed Noise Estimation for Hyperspectral Images Using Multiple Linear Regression

Sun

Nian

2020

Remote Sensing

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HSIs (hyperspectral images) obtained by new-generation hyperspectral sensors contain both electronic noise and photon noise with comparable power. Therefore, both the SI (signal-independent) component and the SD (signal-dependent) component have to be considered. In this paper, a superpixel-based noise estimation algorithm using MLR (multiple linear regression) is proposed for the above mixed noise to estimate the noise standard deviation of both SI component and SD component. First, superpixel segmentation is performed on the first principal component obtained by MNF (minimum noise fraction)-based dimensionality reduction to generate non-overlapping regions with similar pixels. Then, MLR is performed to remove the spectral correlation, and a system of linear equations with respect to noise variances is established according to the local sample statistics calculated within each superpixel. By solving the equations in terms of the least-squares method, the noise variances are determined. The experimental results show that the proposed algorithm provides more accurate local sample statistics, and yields a more accurate noise estimation than the other state-of-the-art algorithms for simulated HSIs. The results of the real-life data also verify the effectiveness of the proposed algorithm.

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