final year mechanical projects in bangalore,chennai
TRANSCRIPT
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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