Method And System For Increasing Signal-To-Noise Ratio

• US 20110170796A1
• Filed: 01/29/2007
• Published: 07/14/2011
• Est. Priority Date: 01/31/2006
• Status: Active Grant

• Current Assignee: Canadian Space Agency (Government of Canada)
• Original Assignee: Canadian Space Agency (Government of Canada)
• Inventors: Qian, Shen-En, Othman, Hisham
• Granted Patent: US 8,358,866 B2
• Time in Patent Office: Days
• US Class Current: 382/264
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/10036 Multispectral image; Hyperspectral image
• CPC Class Codes: G06T 2207/20064 Wavelet transform [DWT]
• CPC Class Codes: G06T 2207/30181 Earth observation
• CPC Class Codes: G06T 5/002 Denoising; Smoothing noise processing or correction adapted to be used in an image pickup device containing and electronic image sensor H04N23/81, H04N25/60 - H04N25/67
• CPC Class Codes: G06T 5/10 using non-spatial domain filtering
• CPC Class Codes: G06T 5/20 using local operators
• CPC Class Codes: G06T 5/50 using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 5/70 Denoising; Smoothing

Abstract

Methods and systems for increasing the signal-to-noise ratio for satellite sensor data or signals, such as hyperspectral imageries (also referred to as datacubes due to their 3-dimensional nature). This is done by reducing the noise in the data or signals by first elevating the noise level temporarily for effective denoising. The denoising process is then performed in this condition and the noise level is then reversibly de-elevated after denoising. The denoising process comprises noise removal in both the spectral and the spatial domains. Once the denoising process is complete, the data is converted back from the spectral and spatial domains. Since this reconstruction process introduces errors, these errors are compensated for using the components from both the original data and denoised data filtered by the low pass filters.

Claims

1. A method for improving a signal to noise ratio of data in a multidimensional datacube, said data being in an original domain, the method comprising the steps of:

a) elevating a noise level of said data

b) removing noise from said data in a spatial domain

c) removing noise from said data in a spectral domain to result in denoised data

d) converting denoised data into said original domain

e) correcting errors introduced to said data by step d).

2. A method according to claim 1 wherein step a) is accomplished by transforming said datacube into a spectral derivative domain.

3. A method according to claim 2 wherein said datacube is transformed to said spectral derivative domain by using a formula

4. A method according to claim 1 wherein step b) is accomplished by a series of steps including computing a 2-d wavelet transform for each spectral band image of a spectral derivative of said datacube.

5. A method according to claim 4 wherein said series of steps further comprises:

estimating a threshold value for each spectral band image

performing a soft threshold wavelet shrinkage operation

computing an inverse 2-D wavelet transform

6. A method according to claim 5 wherein said inverse 2D wavelet transform comprises:

{tilde over (θ

)}(λ

)=IDWT2{η

_spatial(DWT2{θ

(θ

)})} λ

=1,2,3, . . . ,N_b

wherein

θ

(λ

) is a spectral derivative of a band image at band A of said datacube

{tilde over (θ

)}(λ

) is a spatially denoised derivative spectral band image of said datacube

DWT2 is a 2-D discrete wavelet transform applied to said spectral derivative of said band images at two spatial dimensions (along-track and across-track dimensions)

IDWT2 is an associated 2-D inverse discrete wavelet transform.

η

_spatial is a threshold function applied to said band image on a band-by-band basis for the entire said datacube.

7. A method according to claim 1 wherein step c) is accomplished by a series of steps including computing a 1-D wavelet transform for each spectrum corresponding to a spatial pixel at each location at a spatially denoised datacube resulting from step b).

8. A method according to claim 7 wherein said series of steps further comprises:

estimating a threshold value for each spectrum

performing a soft threshold wavelet shrinkage operation

computing an inverse 1-D wavelet transform

9. A method according to claim 8 wherein said inverse 1-D wavelet transform comprises:

{tilde over (θ

)}(p,l)=IDWT2{η

_spatial(DWT{{tilde over (θ

)}(p,l)})} p=1,2, . . . ,N_c;

l=1,2, . . . ,N_r

wherein

{tilde over (θ

)}(p,l) is a spectrum derivative of a spatially denoised datacube at spatial location (p,l)

{tilde over (θ

)}(p,l) is a spatially-spectrally denoised spectrum derivative of said datacube at location (p,l)

DWT is a 1-D discrete wavelet transform applied to a spectrum

IDWT is an associated 1-D inverse discrete wavelet transform.

η

_spectral is a threshold function applied to said spectrum on a pixel-by-pixel basis for the entire said datacube.

10. A method according to claim 1 wherein said denoised data is converted into said original domain by spectral integration.

11. A method according to claim 10 wherein said spectral integration is accomplished by using a formula:

12. A method according to claim 9 wherein step e) is accomplished by cancelling error accumulated by step d) and by compensating a cancelled signal.

13. A method according to claim 12 wherein compensating said cancelled signal is accomplished by filtering.

14. A method according to claim 13 wherein at least two identical low pass filters are used to compensate said cancelled signal.

15. A method according to claim 14 wherein said low pass filters are moving average filters.

16. A method according to claim 15 wherein said filters replace low pass frequency components of a denoised signal from step d) with low frequency components of an original signal derived from said datacube such that

17. A system for increasing a signal to noise ratio of multidimensional data, said data being in an original domain, the system comprising:

means for elevating a noise level of said data

means for removing noise from said data in at least one domain to result in denoised data

means for converting denoised data into said original domain

means for correcting errors introduced to said data by a conversion of said denoised data

18. A system according to claim 17 wherein said means for removing noise comprises:

means for removing noise from said data in a spatial domain

19. A system according to claim 17 wherein said means for removing noise comprises:

means for removing noise from said data in a spectral domain

20. A system according to claim 17 wherein said means for correcting errors comprises filters.