Recent Advances and Challenges in Automatic Hyperspectral Endmember Extraction

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

doi:10.1007/978-981-13-1217-5_44

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Further investigation of VD estimation techniques revealed that, these methods are also produce misleading results. More detailed and comparative analysis between various endmember extraction algorithms is performed time to time in [30][31][32][33]. Therefore, to overcome most of the challenges available in recent EE algorithm, there is a great need of a fully automated and robust endmember extraction algorithm.…”

Section: Resultsmentioning

confidence: 99%

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: Resultsmentioning

confidence: 99%

Hyperspectral Endmember Extraction Techniques

Kale¹,

Solankar²,

Nalawade³

2020

Processing and Analysis of Hyperspectral Data

View full text Add to dashboard Cite

show abstract

“…Thus, it is possible to graph the image data in the spectral space, where the display's axes correspond to the images' bands [22]. n-D is commonly used with MNF data from a spatial subset of pure pixels obtained from PPI [31]. The PPI scatterplot makes it easier to select the purest pixels by rotating their axes.…”

Section: E N-dimensional Visualizermentioning

confidence: 99%