Noise Reduction in Hyper spectral Imagesthrough Spectral Unmixing

Existing system

Spectral unmixing and denoising of hyperspectral images have always been

regarded as separate problems. By considering the physical properties of a mixed

spectrum, this paper introduces Unmixing-based Denoising, a supervised

methodology representing any pixel as a linear combination of reference spectra in

a hyperspectral scene. Such spectra are related to some classes of interest, and

exhibit negligible noise influences. “final year engineering projects in Chennai”

.as they are averaged over areas for which ground truth is available. After the

unmixing process, the residual vector is mostly composed by the contributions of

uninteresting materials, http://www.embeddedinnovationlab. com.unwanted

atmospheric influences and sensor-induced noise, and is thus ignored in the

reconstruction of each spectrum. Final year embedded system projects in

Bangalore and Chennai. The proposed method, in spite of its simplicity, is able to

remove noise effectively for spectral bands with both low and high Signal-to-

Noise Ratio. Experiments show that this method could be used to retrieve spectral

information from corrupted bands, such as the ones placed at the edge between

Ultraviolet and visible light frequencies, which are usually discarded in practical

applications. Final year projects in Bangalore and Chennai. The proposed

method achieves better results in terms of visual quality in comparison to

competitors, if the Mean Squared Error is kept constant: this leads to question the

validity of Mean Squared Error as a predictor for image quality in remote sensing

applications.

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Proposed system

“Final year ece projects in Chennai and bangalore” .Spectral mixing is

inherent in any finite-resolution digital imagery of a heterogeneous surface, so that

mixed pixels are inevitably created when multispectral images are scanned.

Solving the spectral mixture problem is, therefore, involved in image classification,

referring to the techniques of spectral unmixing. “Final year mechanical

engineering projects in Bangalore and Chennai”. The invention of imaging

spectrometers especially promotes the potential of applying spectral unmixing for

sub-pixel classification. This paper investigates two spectral unmixing techniques:

the least squares (LS) unmixing and the matched filter (MF) unmixing.

Experiments with a set of AVIRIS data were carried out to evaluate the

performance of spectral unmixing. “final year eee projects in Bangalore and

Chennai.” The MF unmixing method proved itself to be an effective technique in

classifying a hyperspectral image by showing a 90% classification accuracy.

Whereas, the LS unmixing technique did not show promising results, when it was

applied to the original bands of the test image. The maximum noise fraction

(MNF) transformation, however, is found to be helpful to promote the performance

of the LS unmixing. Applying the LS unmixing to the MNF transformed images

can improve the classification accuracy for about 20%.

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KEYWORDS

FINAL YEAR ECE PROJECTS IN CHENNAI AND

COIMBATORE

Final year eee projects in bangalore and Chennai

Final year vlsi projects in bangalore and Chennai

Final year mechanical engineering projects in chennai

and Bangalore

Final year engineering projects in chennai

Final year mechanical projects in chennai and

Bangalore

Final year embedded system projects in chennai and

Coimbatore

Address:

Bangalore,Chennai,Coimbatore

http://www.embeddedinnovationlab.com

http://www.embeddedinnovationlab.com