A Research Review on Hyperspectral Data Processing and Analysis Algorithms

Kale, K. V.; Solankar, Mahesh M.; Nalawade, Dhananjay B.; Dhumal, Rajesh K.; Gite, Hanumant R.

doi:10.1007/s40010-017-0433-y

Cited by 43 publications

(36 citation statements)

References 63 publications

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“…In HSI data, finding endmembers is a critical task due to their population within the data is significantly low and generally beyond the human visual perception [6]. Once the endmembers are extracted precisely from the HSI image itself, they can be used as a reference data or training data to enhance the classification accuracy or efficient spectral unmixing of the hyperspectral images [1]. These image derived endmembers are more effective as compared to the standard laboratory or field spectra, as they are not certainly recorded under the similar conditions as the satellite or airborne HSI data.…”

Section: Hyperspectral Endmembersmentioning

confidence: 99%

“…The hyperspectral imaging (HSI) is a renowned technology that uses the chemical composition based spectroscopic properties of earth surface materials for their detailed analysis and exploration [1]. HSI is spectrally overdetermined and has the ability to observe the surface materials continuously across the wide range of electromagnetic spectrum, generally covering 0.4-2.5 μm, where different regions of wavelengths gives information about different material contents [2].…”

Section: Introductionmentioning

confidence: 99%

“…Along with the ample amount of information, the hyperspectral imaging is characterized by several data processing and analysis challenges including adverse atmospheric effects, curse of dimensionality, unavailability of the ground-truth information and spectral mixing [1][2][3]. To extract the maximum information from this high volume HSI data with greater precision, these challenges needs to be handled efficiently.…”

Section: Introductionmentioning

confidence: 99%

“…Of course, there several popular digital image processing and pattern recognition techniques are well available in the literature, but those techniques cannot be used as it is to process HSI data due to its different challenges. To process and analyses the hyperspectral data proficiently with better accuracy, there is a need to follow the generalized and application independent data processing framework ( Figure 1) [1].…”

Section: Introductionmentioning

confidence: 99%

“…This leads to the linearly combined spectral reflectance of the component materials for every mixed pixel and keep that particular pixel away from any of the class in classification. To resolve these significant obstacles of HSI data classification and mapping, the term, image derived "endmember" comes into picture and contribute with remarkable role in increasing HSI data classification accuracy [1].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Hyperspectral Endmember Extraction Techniques

Kale¹,

Solankar²,

Nalawade³

2020

Processing and Analysis of Hyperspectral Data

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Hyperspectral data processing and analysis mainly plays a vital role in detection, identification, discrimination and estimation of earth surface materials. It involves atmospheric correction, dimensionality reduction, endmember extraction, spectral unmixing and classification phases. One of the ultimate aims of hyperspectral data processing and analysis is to achieve high classification accuracy. The classification accuracy of hyperspectral data most probably depends upon image-derived endmembers. Ideally, an endmember is defined as a spectrally unique, idealized and pure signature of a surface material. Extraction of consistent and desired endmember is one of the important criteria to achieve the high accuracy of hyperspectral data classification and spectral unmixing. Several methods, strategies and algorithms are proposed by various researchers to extract the endmembers from hyperspectral imagery. Most of these techniques and algorithms are significantly dependent on user-defined input parameters, and this issue is subjective because there is no standard specificity about these input parameters. This leads to inconsistencies in overall endmember extraction. To resolve the aforementioned problems, systematic, generic, robust and automated mechanism of endmember extraction is required. This chapter gives and highlights the generic approach of endmember extraction with popular algorithm limitations and challenges.

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Section: Hyperspectral Endmembersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%