Estimation of Noise Model Parameters for Images Taken by a Full-frame Hyperspectral Camera

Date

2015-09-23

Author

DEMİRKESEN, Can
Leloğlu, Uğur Murat

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

239
views

0
downloads

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 Hyper Spectral Images (his). 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.

Subject Keywords

Hyperspectral, Noise estimation, Region growing, Radiometric calibration

URI

https://hdl.handle.net/11511/31859

DOI

https://doi.org/10.1117/12.2195057

Collections

Graduate School of Natural and Applied Sciences, Conference / Seminar

Suggestions

OpenMETU
Core

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 ...
Chernoff Fusion of Gaussian Mixtures for Distributed Maneuvering Target Tracking GUNAY, Melih; Orguner, Umut; Demirekler, Mübeccel (2015-07-09) A fusion methodology for tracks represented by Gaussian mixtures is proposed for distributed maneuvering target tracking with unknown correlation information between the local agents. For this purpose, Chernoff fusion is applied to the Gaussian mixtures provided by the local interacting multiple-model (IMM) filters. Chernoff fusion of Gaussian mixtures is achieved using a recently proposed method in the literature involving a sigma-point approximation. The results show that the fusion of Gaussian mixtures i...
Rigorous Analysis of Deformed Nanowires Using the Multilevel Fast Multipole Algorithm Karaosmanoglu, Bariscan; Yilmaz, Akif; Ergül, Özgür Salih (2015-05-17) We present accurate full-wave analysis of deformed nanowires using a rigorous simulation environment based on the multilevel fast multipole algorithm. Single nanowires as well as their arrays are deformed randomly in order to understand the effects of deformations to scattering characteristics of these structures. Results of hundreds of simulations are considered for statistically meaningful analysis of deformation effects. We show that deformations significantly enhance the forward-scattering abilities of ...
Computation of radar cross sections of complex targets by shooting and bouncing ray method Özgün, Salim; Kuzuoğlu, Mustafa; Department of Electrical and Electronics Engineering (2009) In this study, a MATLAB® code based on the Shooting and Bouncing Ray (SBR) algorithm is developed to compute the Radar Cross Section (RCS) of complex targets. SBR is based on ray tracing and combine Geometric Optics (GO) and Physical Optics (PO) approaches to compute the RCS of arbitrary scatterers. The presented algorithm is examined in two parts; the first part addresses a new aperture selection strategy named as “conformal aperture”, which is proposed and formulated to increase the performance of the cod...
Noise Estimation for Hyperspectral Imagery using Spectral Unmixing and Synthesis DEMİRKESEN, CAN; Leloğlu, Uğur Murat (2014-09-25) 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 wit...

Citation Formats

C. DEMİRKESEN and U. M. Leloğlu, “Estimation of Noise Model Parameters for Images Taken by a Full-frame Hyperspectral Camera,” 2015, vol. 9643, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/31859.