Noise has to be taken into account in the algorithms of classification, target detection and anomaly detection. Recent studies indicate that noise estimation is also crucial in subspace identification of HSI. Several techniques were proposed for noise estimation including: multiple linear regression based techniques, spectral unmixing and remixing etc. The noise in HSI is widely accepted to be a spatially stationary random process. But the variance of the noise varies from one wavelength to another. Two types of noise are considered: the first one is the circuitry noise (thermal noise) which is signal independent. The second one is the photonic noise (shot noise) which is signal dependent. The latter is considered to be the dominant one. A reliable way to accurately estimate the noise requires the identification of a large uniform region in the image. To this end, we propose a region growing technique. At the end of this process, a certain number of regions with different sizes and uniformities are obtained. The next step consists of identifying the most uniform region having the largest area. Once the most uniform and largest region of the scene is identified the next step is to apply an ideal low pass filter to this region. This yields an estimate of the noise-free data, hence the noise itself by calculating the difference. It is also possible to apply the well-known scatter plot technique. Experiments suggest that the proposed scheme produces comparable results to its competitors. A major advantage of the technique is the automated identification of an homogenous region.
Detection of region boundaries is a very challenging task especially in the presence of noise or speckle as in synthetic aperture radar images. In this work, we propose a user interaction based boundary detection technique which makes use of B-splines and well-known powerful tools of information theory such as the Kullback-Leibler divergence (KLD) and Bhattacharyya distance. The proposed architecture consists of the following four main steps: (1) The user selects points inside and outside of a region. (2) Profiles that link these inside and outside points are extracted. (3) Boundary points that lie on the profile are located. (4) Finally, the B-splines that provide both elasticity and smoothness are used connect boundary points together to obtain an accurate estimate of the actual boundary. Existing work related to this approach are extended in several axes. First the use of multiple points both inside and outside of a region made possible to obtain a few times more boundary points. A tracking stage is proposed to put the boundary points in the right order and at the same time eliminate some of them that are erroneously detected as boundary points as well. Experiments were conducted using simulated and real SAR images.
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 denoising
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 NFINDR
after manual selection of number of end-members and the image is reconstructed using the estimated endmembers
and abundances. Actually, image de-noising and noise estimation are two sides of the same coin: Once we denoise
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.
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