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Subspace based striping noise reduction in hyperspectral images
Subspace based striping noise reduction in hyperspectral images
Subspace based striping noise reduction in hyperspectral images
Subspace based striping noise reduction in hyperspectral images
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Subspace based striping noise reduction in hyperspectral images

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  • 1. Subspace-Based Striping Noise Reductionin Hyperspectral ImagesABSTRACTIn this paper, a new algorithm for striping noise reduction in hyperspectral images isproposed. The new algorithm exploits the orthogonal subspace approach to estimate thestriping component and to remove it from the image, preserving the useful signal. Thealgorithm does not introduce artifacts in the data and also takes into account thedependence on the signal intensity of the striping component. The effectiveness of thealgorithm in reducing striping noise is experimentally demonstrated on real data acquiredboth by airborne and satellite hyperspectral sensors.Existing SystemThe existing system available for fuzzy filters for noise reduction deals with fat-tailed noise like impulse noise and median filter. Only Impulse noise reduction using fuzzy filters Gaussian noise is not specially concentrated It does not distinguish local variation due to noise and due to image structure.Proposed SystemThe proposed system presents a new technique for filtering narrow-tailed andmedium narrow-tailed noise by a fuzzy filter. The system, First estimates a “fuzzy derivative” in order to be less sensitive to local variationsdue to image structures such as edgeswww.nanocdac.com www.nsrcnano.com branches: hyderabad nagpur
  • 2.  Second, the membership functions are adapted accordingly to the noise level toperform “fuzzy smoothing.” For each pixel that is processed, the first stage computes a fuzzy derivative.Second, a set of 16 fuzzy rules is fired to determine a correction term. These rulesmake use of the fuzzy derivative as input. Fuzzy sets are employed to represent the properties, while the membershipfunctions for and are fixed, the membership function for is adapted after eachiteration.HARDWARE REQUIREMENTS• SYSTEM : Pentium IV 2.4 GHz• HARD DISK : 40 GB• MONITOR : 15 VGA colour• MOUSE : Logitech.• RAM : 256 MB• KEYBOARD : 110 keys enhanced.SOFTWARE REQUIREMENTS• Operating system : Windows XP Professional• Front End : JAVA.• Tool : Eclipse 3.3MODULES USED• Pre Processing• Member function• Fuzzy Smoothingwww.nanocdac.com www.nsrcnano.com branches: hyderabad nagpur
  • 3. • Get Clear Gray ImageMODULE DESCRIPTIONPre ProcessingFirst estimates a “fuzzy derivative” in order to be less sensitive to local variationsdue to image structures such as edgesSecond, the membership functions are adapted accordingly to the noise level to perform“fuzzy smoothing.”Member functionFor each pixel that is processed, the first stage computes a fuzzy derivative.Second, a set of fuzzy rules is fired to determine a correction term. These rules make useof the fuzzy derivative as input.Fuzzy sets are employed to represent the properties, and while the membership functionsfor and is fixed, the membership function for are adapted after each iteration.Fuzzy SmoothingSet the calculated member function value from processing of gray scale Image tothe negative pixel areaGet Clear Gray Imagewww.nanocdac.com www.nsrcnano.com branches: hyderabad nagpur
  • 4. To view the clear image by user this very particular module is used.REFERENCE:N.Acito, M.Diani, and G.Corsini, “Subspace-based striping noise reduction inhyperspectral images”, IEEE Transactions on Geoscience and Remote Sensing, Vol.49,April 2011.www.nanocdac.com www.nsrcnano.com branches: hyderabad nagpur

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