A Novel Algorithm for Classifying Vegetation and Non-Vegetation Using Red Edge Slope Coefficient with Hyperspectral LCTF Imaging

Bayarsaikhan Uudus; Bayartsetseg Oyungerel; Yukihiro Takahashi; Erdenebaatar Dashdondog; Turtogtokh Tumenjargal; Narangarvuu Dashdondog; Qinxue Wang; Hasi Bagan; Begzsuren Tumendemberel

doi:10.20944/preprints202405.0433.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 6 May 2024 / Approved: 7 May 2024 / Online: 8 May 2024 (15:28:55 CEST)

Hyperspectral imaging, a rapidly evolving technique, furnishes exhaustive information on Earth's surface reflectance over an extensive spectrum of wavelengths. We have developed a more precise method for analyzing spectral data by meticulously selecting plant leaf pixels and employing a relative reference approach. This enhanced technique correlates camera pixel values with light intensity, bypassing the need for complex equations and thereby providing clearer insights into vegetation vitality. The findings of this study not only enhance the precision of hyperspectral imaging but also bolster our capability for ecological monitoring and understanding environmental health. A novel algorithm based on the red-edge slope was established to classify images, exploiting the spectral reflectance data gleaned from the hyperspectral Liquid Crystal Tunable Filter (LCTF) camera. Compared with conventional SVM and K-means algorithms, our novel algorithm demonstrated commendable performance, achieving an overall accuracy of 90% with a Kappa Coefficient of 0.86. This comparison highlights the novel algorithm's relative strength and potential for large-scale satellite and drone imagery analysis applications. The advancements facilitated by this research are instrumental in ecological assessments, conservation efforts, and resource management strategies, signaling a leap forward in the era of environmental monitoring and analysis.

Hyperspectral Imaging; Liquid Crystal Tunable Filter; Red Edge Slope Coefficient; Spectral Reflectance; Vegetation Classification; Remote Sensing Algorithms

Environmental and Earth Sciences, Ecology

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A Novel Algorithm for Classifying Vegetation and Non-Vegetation Using Red Edge Slope Coefficient with Hyperspectral LCTF Imaging

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