Noise Estimation for Hyperspectral Imagery using Spectral Unmixing and Synthesis

2014-09-25
DEMİRKESEN, CAN
Leloğlu, Uğur Murat
Most hyperspectral image (HSI) processing algorithms assume a signal to noise ratio model in their formulation which makes them dependent on accurate noise estimation. Many techniques have been proposed to estimate the noise. A very comprehensive comparative study on the subject is done by Gao et al. [1]. In a nut-shell, most techniques are based on the idea of calculating standard deviation from assumed-to-be homogenous regions in the image. Some of these algorithms work on a regular grid parameterized with a window size w, while others make use of image segmentation in order to obtain homogenous regions. This study focuses not only to the statistics of the noise but to the estimation of the noise itself. A noise estimation technique motivated from a recent HSI de-noising approach [2] is proposed in this study. The de-noising algorithm is based on estimation of the end-members and their fractional abundances using non-negative least squares method. The end-members are extracted using the well-known simplex volume optimization technique called N-FINDR after manual selection of number of end-members and the image is reconstructed using the estimated end-members and abundances. Actually, image de-noising and noise estimation are two sides of the same coin: Once we de-noise an image, we can estimate the noise by calculating the difference of the de-noised image and the original noisy image. In this study, the noise is estimated as described above. To assess the accuracy of this method, the methodology in [1] is followed, i.e., synthetic images are created by mixing end-member spectra and noise. Since best performing method for noise estimation was spectral and spatial de-correlation (SSDC) originally proposed in [3], the proposed method is compared to SSDC. The results of the experiments conducted with synthetic HSIs suggest that the proposed noise estimation strategy outperforms the existing techniques in terms of mean and standard deviation of absolute error of the estimated noise. Finally, it is shown that the proposed technique demonstrated a robust behavior to the change of its single parameter, namely the number of end-members.

Suggestions

SNR CALCULATION METHOD FOR REMOTE SENSING SATELLITE IMAGING SYSTEMS
Turkmenoglu, Mustafa; Sengul, Orhan; Demircioglu, Erdem (2013-06-01)
Signal to Noise Ratio (SNR) is a metric used to link the image quality and radiometric performance of the remote sensing imaging systems. It is one of the remote sensing imaging system's design parameters that represents the image quality. SNR calculation and analysis should be carried out at design phase of remote sensing imaging systems. This calculation and analysis are crucial for confirmation of design success. It is important to show that the light flux reaching the sensor and the generated electrons ...
Electromagnetic target recognition with the fused MUSIC spectrum matrix method: Applications and performance analysis for incomplete frequency data
Secmen, Mustafa; Ekmekci, Evren; Sayan, Gönül (2007-01-01)
The aim of this paper is to apply an electromagnetic target recognition method, which is based on the use of fused MUSIC spectrum matrices, to the case of incomplete frequency domain data. The aforementioned method was suggested recently and succesfully applied to both canonical and complicated targets in the presence of complete frequency domain data [1]. However, most of the real world applications involve the use of severely incomplete frequency data, especially missing low frequency information. In this...
Dimension reduced robust beamforming for towed arrays
Topçu, Emre; Candan, Çağatay; Department of Electrical and Electronics Engineering (2015)
Adaptive beamforming methods are used to obtain higher signal to interference plus noise ratio at the array output. However, these methods are very sensitive to steering vector and covariance matrix estimation errors. To overcome this issue, robust methods are usually employed. On the other hand, implementation of these robust methods can be computationally expensive for arrays with large number of sensors. Reduced dimension techniques aim to lower the computational load of adaptive beamforming algorithms w...
Covariance Matrix Estimation of Texture Correlated Compound-Gaussian Vectors for Adaptive Radar Detection
Candan, Çağatay; Pascal, Frederic (2022-01-01)
Covariance matrix estimation of compound-Gaussian vectors with texture-correlation (spatial correlation for the adaptive radar detectors) is examined. The texture parameters are treated as hidden random parameters whose statistical description is given by a Markov chain. States of the chain represent the value of texture coefficient and the transition probabilities establish the correlation in the texture sequence. An Expectation-Maximization (EM) method based covariance matrix estimation solution is given ...
Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances
Ardeshiri, Tohid; Özkan, Emre; Orguner, Umut; Gustafsson, Fredrik (2015-12-01)
We present an adaptive smoother for linear state-space models with unknown process and measurement noise covariances. The proposed method utilizes the variational Bayes technique to perform approximate inference. The resulting smoother is computationally efficient, easy to implement, and can be applied to high dimensional linear systems. The performance of the algorithm is illustrated on a target tracking example.
Citation Formats
C. DEMİRKESEN and U. M. Leloğlu, “Noise Estimation for Hyperspectral Imagery using Spectral Unmixing and Synthesis,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31820.