A comparative study of signal transformation techniques in automated spectral unmixing of infrared spectra for remote sensing applications

Singh, Keshav Dev; Ramakrishnan, D.

doi:10.1080/01431161.2017.1280625

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…Fig. 7 shows the fractional abundance images for each endmember, obtained from the Discrete Wavelet Transformation (DWT) based linear spectral unmixing [6,7]. In these abundance maps, the dark black pixels represent the lowest fractions value (0), different shades of grey color indicate increasing abundance, and exactly bright white pixels indicate the maximum fractions value (1).…”

Section: Resultsmentioning

confidence: 99%

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Uncertainties in TIR Hyperspectral Image Cube Unmixing

Singh¹

2017

ujg

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The spectral unmixing in the thermal infrared (TIR) region is not a linear case. For simplicity, the spectral mixing for pixel deconvolution is assumed to be linear, but in reality, the intimate mixture spectra are non-linear. The multiple scattering effects due to texture and fabric do affect the spectral shape and form. In this paper, the uncertainties in unmixing of TIR hyperspectral image cube are investigated.

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Section: Resultsmentioning

confidence: 99%

“…After the derived endmembers, the Pearson Correlation Coefficient (PCC) is used to match these endmembers to each pixel of the image scene by Linear Mixing Model (LMM) developed in [6,7]. Here, an iterative procedure based fully constrained quadratic programming algorithm is used to unmix each pixel of the hypercube [8].…”

Section: Spectral Unmixingmentioning

confidence: 99%

Uncertainties in TIR Hyperspectral Image Cube Unmixing

Singh¹

2017

ujg

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“…However, this strategy might discard relevant signatures in the presence of many mixed pixels. Another work proposed to compare only pure pixels extracted from the HI with the library spectra in the wavelet domain [277].…”

Section: B Library Pruning Techniquesmentioning

confidence: 99%

Spectral Variability in Hyperspectral Data Unmixing: A comprehensive review

Borsoi

Imbiriba

Bermudez

et al. 2021

IEEE Geosci. Remote Sens. Mag.

145

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The spectral signatures of the materials contained in hyperspectral images (HI), also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an HI. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what propagates significant mismodeling errors throughout the whole unmixing process and compromises the quality of its results. Therefore, large efforts have been recently dedicated to mitigate the effects of spectral variability in SU. This resulted in the development of algorithms that incorporate different strategies to allow the EMs to vary within an HI, using, for instance, sets of spectral signatures known a priori, Bayesian, parametric, or local EM models. Each of these approaches has different characteristics and underlying motivations. This paper presents a comprehensive literature review contextualizing both classic and recent approaches to solve this problem. We give a detailed evaluation of the sources of spectral variability and their effect in HI spectra. Furthermore, we propose a new taxonomy that organizes existing work according to a practitioner's point of view, based on the necessary amount of supervision and on the computational cost they require. We also review methods used to construct spectral libraries (which are required by many SU techniques) based on the observed HI, as well as algorithms for library augmentation and reduction. Finally, we conclude the paper with some discussions and an outline of possible future directions for the field.

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“…The techniques are included in the software as standard as they have been used for the analysis of remote sensing data (SFF [73][74][75], SAM [76][77][78], BE [79][80][81]). For the analysis of heritage based hyperspectral data only SAM appears to have been used previously [42,82,83].…”

Section: Spectralonmentioning

confidence: 99%

Comparing the effectiveness of hyperspectral imaging and Raman spectroscopy: a case study on Armenian manuscripts

et al. 2018

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There is great practical and scholarly interest in the identification of pigments in works of art. This paper compares the effectiveness of the widely used Raman Spectroscopy (RS), with hyperspectral imaging (HSI), a reflectance imaging technique, to evaluate the reliability of HSI for the identification of pigments in historic works of art and to ascertain if there are any benefits from using HSI or a combination of both. We undertook a case study based on six Armenian illuminated manuscripts (eleventh-eighteenth centuries CE) in the Bodleian Library, University of Oxford. RS, and HSI (380-1000 nm) were both used to analyse the same 10 folios, with the data then used to test the accuracy and efficiency of HSI against the known results from RS using reflectance spectra reference databases compiled by us for the project. HSI over the wavelength range 380-1000 nm agreed with RS at best 93% of the time, and performance was enhanced using the SFF algorithm and by using a database with many similarities to the articles under analysis. HSI is significantly quicker at scanning large areas, and can be used alongside RS to identify and map large areas of pigment more efficiently than RS alone. HSI therefore has potential for improving the speed of pigment identification across manuscript folios and artwork but must be used in conjunction with a technique such as RS.

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A comparative study of signal transformation techniques in automated spectral unmixing of infrared spectra for remote sensing applications

Cited by 7 publications

References 38 publications

Uncertainties in TIR Hyperspectral Image Cube Unmixing

Uncertainties in TIR Hyperspectral Image Cube Unmixing

Spectral Variability in Hyperspectral Data Unmixing: A comprehensive review

Comparing the effectiveness of hyperspectral imaging and Raman spectroscopy: a case study on Armenian manuscripts

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