Assessing Noise Amplitude in Remotely Sensed Images Using Bit-Plane and Scatterplot Approaches

The problem of assessing the noise amplitude affecting remotely sensed hyperspectral images and the corresponding signal-to-noise ratio is discussed. An original algorithm for noise estimation, which performs the analysis of image bit-planes in order to assess their randomness, is described. Differently from more traditional signal-to-noise estimators, which need a homogeneous area in the concerned image to isolate noise contributions, this estimator is almost insensitive to scene texture, a circumstance that allows the developed method to carefully assess the noise amplitude of nearly any observed targets. The developed algorithm has been compared with the well-known noise estimator scatterplot method, for which a novel implementation based on the Hough transform is presented. Hyperspectral and multispectral data cubes collected by the following aerospace imagers, MIVIS, VIRS-200, and MOMS-2P on PRIRODA, have been utilized for investigating the performance of the two considered estimators. Outcomes from processing synthetic and natural images are presented and discussed along this paper.

[1]  Ivan Pippi,et al.  Object recognition by edge analysis: a case study , 1999 .

[2]  H K Huang,et al.  Assessment of noise in a digital image using the join-count statistic and the Moran test. , 1992, Physics in medicine and biology.

[3]  Jean Pierre Veran,et al.  COMPRESSION Software for Astronomical Images , 1994 .

[4]  José M. Bioucas-Dias,et al.  Vertex component analysis: a fast algorithm to unmix hyperspectral data , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Gavin S. P. Miller,et al.  The definition and rendering of terrain maps , 1986, SIGGRAPH.

[6]  Jennifer L. Dungan,et al.  Estimation of signal-to-noise: a new procedure applied to AVIRIS data , 1989 .

[7]  Carver A. Mead,et al.  Charge transfer in charge-coupled devices , 1972 .

[8]  P. Curran.  The semivariogram in remote sensing: An introduction , 1988 .

[9]  John Immerkær,et al.  Fast Noise Variance Estimation , 1996, Comput. Vis. Image Underst..

[10]  Luciano Alparone,et al.  Estimating noise and information of multispectral imagery , 2002 .

[11]  Ram M. Narayanan,et al.  Noise estimation in remote sensing imagery using data masking , 2003 .

[12]  Qian Du,et al.  Interference and noise-adjusted principal components analysis , 1999, IEEE Trans. Geosci. Remote. Sens..

[13]  Chein-I Chang,et al.  Estimation of number of spectrally distinct signal sources in hyperspectral imagery , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Luciano Alparone,et al.  Reliably estimating the speckle noise from SAR data , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[15]  A. Barducci,et al.  Analysis and rejection of systematic disturbances in hyperspectral remotely sensed images of the Earth. , 2001, Applied optics.

[16]  Ramesh C. Jain,et al.  Segmentation through Variable-Order Surface Fitting , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  T. S. Lomheim,et al.  General noise processes in hybrid infrared focal plane arrays , 1991 .

[18]  J.S. Lee,et al.  Noise Modeling and Estimation of Remotely-Sensed Images , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[19]  Carlo Berzuini,et al.  Teoria della Probabilitá e Ragionamento Automatico , 1991 .

[20]  W Ruyten.  Smear correction for frame transfer charge-coupled-device cameras. , 1999, Optics letters.

[21]  Søren I. Olsen,et al.  Estimation of Noise in Images: An Evaluation , 1993, CVGIP Graph. Model. Image Process..

[22]  Kenneth Watson,et al.  Processing remote sensing images using the 2-D FFT; noise reduction and other applications , 1993 .

[23]  Luciano Alparone,et al.  Information-theoretic assessment of sampled hyperspectral imagers , 2001, IEEE Trans. Geosci. Remote. Sens..

[24]  Erwin Keeve,et al.  Entropic estimation of noise for medical volume restoration , 2002, Object recognition supported by user interaction for service robots.

[25]  R Glenn Sellar,et al.  Comparison of relative signal-to-noise ratios of different classes of imaging spectrometer. , 2005, Applied optics.

[26]  J. Nieke,et al.  Noise contributions for imaging spectrometers. , 1999, Applied optics.

[27]  R. Unbehauen,et al.  Estimation of image noise variance , 1999 .

[28]  Alessandro Mecocci,et al.  Theoretical and experimental assessment of noise effects on least-squares spectral unmixing of hyperspectral images , 2005 .

[29]  Azriel Rosenfeld,et al.  A Fast Parallel Algorithm for Blind Estimation of Noise Variance , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Eric Dubois,et al.  Fast and reliable structure-oriented video noise estimation , 2005, IEEE Transactions on Circuits and Systems for Video Technology.