Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Entomological Remote Dark Field Signal Extraction by Maximum Noise Fraction and Unsupervised Clustering for Species Identification

Version 1 : Received: 11 March 2021 / Approved: 12 March 2021 / Online: 12 March 2021 (20:16:55 CET)

How to cite: Benoit, K.; Assoi, E.K.; Gbogbo, A.Y.; Zoueu, J. Entomological Remote Dark Field Signal Extraction by Maximum Noise Fraction and Unsupervised Clustering for Species Identification. Preprints 2021, 2021030352 (doi: 10.20944/preprints202103.0352.v1). Benoit, K.; Assoi, E.K.; Gbogbo, A.Y.; Zoueu, J. Entomological Remote Dark Field Signal Extraction by Maximum Noise Fraction and Unsupervised Clustering for Species Identification. Preprints 2021, 2021030352 (doi: 10.20944/preprints202103.0352.v1).

Abstract

Characterization of flying insects in-situ measurement using remote sensing spectroscopy is an emerging research field. Also, most analysis techniques in remote sensing spectroscopy are based on the use of an intensity threshold which introduces indeterminacies in the number of detected specimens. In this manuscript, we investigated the possibility of analysing passive remote sensing spectroscopy measurement data using the maximum noise fraction method. The results obtained show that this analysis technique can help to overcome the measurement of background noise in spectroscopic measurements.

Subject Areas

remote sensing; spectroscopy; blind source separation; unsupervised clustering; insects

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