Abstract
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.
Highlights
Emerging from the development of hyperspectral remote-sensing technology, Hyperspectral Images (HSI) have both high spatial resolution and spectral resolution
The electronic noise is an additive noise generated by electronic circuitry, which is independent of the signal, while the photon noise is generated by the Poisson-distributed number of photons, which is dependent on the signal
Exploiting the spectral and spatial correlation for noise estimation, Roger et al, perform Multiple Linear Regression (MLR) in small blocks of the same size, and the average of the variances of the residuals for all blocks is taken as the estimated variance for each band [9]
Summary
Emerging from the development of hyperspectral remote-sensing technology, HSIs (hyperspectral images) have both high spatial resolution and spectral resolution. Exploiting the spectral and spatial correlation for noise estimation, Roger et al, perform MLR (multiple linear regression) in small blocks of the same size, and the average of the variances of the residuals for all blocks is taken as the estimated variance for each band [9] Since this estimation is sensitive to a heterogeneous subset in a small block, Aiazzi et al, focus on estimating the information conveyed to a user by HSIs, and propose an estimation algorithm under the assumption that the variance of the observed signal measured on homogeneous areas will be equal to the variance of the noise [10]. The local expectation and variance pairs in homogenous regions are viewed as scatter-points clustered along a straight line, whose slope and intercept measure the variance of the SD noise and the SI noise, respectively This process is performed on the HSIs band by band.
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