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Hyperpectral Image : Single Spectrum Analysis And Reflection Of Image

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Hyperspectral imaging (HSI) order has progressed toward becoming a well known research point as of late, and compelling include extraction is a vital stride before the arrangement undertaking. Generally, Spectral component extraction methods are connected to the HSI information shape straightforwardly. This paper shows a novel calculation for HSI include extraction by abusing the curvelet transformed space by means of a moderately new unearthly component preparing procedure—solitary range examination (SSA). In spite of the fact that the wavelet change has been generally connected for HSI information examination, the curvelet change is utilized in this paper since it is capable to isolate picture geometric subtle elements and foundation …show more content…

The use of the support vector machine (SVM) as a classifier for HSI applications has been shown to be robust and highly accurate [5]–[7]. The samples or pixels are evaluated in SVM by means of their respective features or spectral bands, which can contribute to more robust discrimination as they include information from different spectral wavelengths. However, HSI data are usually prone to noise, which can reduce the discrimination ability limiting the accuracy in classification tasks. For that reason, there is great interest for a potential decomposition of the spectral profiles into components in such a way that noise could be removed or mitigated by avoiding particular components with high noisy content. In this decomposition context, a particularly interesting research area is the use of the empirical mode decomposition (EMD) technique applied in 1-D to the spectral profile of the pixels as briefly evaluated in [8]. The EMD is the main part of the Hilbert–Huang transform, an algorithm for the analysis of nonlinear and nonstationary time series [9], [10]. EMD decomposes a 1-D signal into a few components called intrinsic mode functions (IMFs) and has been widely used in processing signal applications such as speech recognition [11]. The reconstruction of the 1-D signal by some of their IMFs would provide an alternative signal that could be easier to classify. Although this was the objective, an evaluation in [8] showed no improvement at all but

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