Hyperspectral Image Dataset for Individual Penguin Identification

Autor:	Noboru, Youta, Ozasa, Yuko, Tanaka, Masayuki
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Remote individual animal identification is important for food safety, sport, and animal conservation. Numerous existing remote individual animal identification studies have focused on RGB images. In this paper, we tackle individual penguin identification using hyperspectral (HS) images. To the best of our knowledge, it is the first work to analyze spectral differences between penguin individuals using an HS camera. We have constructed a novel penguin HS image dataset, including 990 hyperspectral images of 27 penguins. We experimentally demonstrate that the spectral information of HS image pixels can be used for individual penguin identification. The experimental results show the effectiveness of using HS images for individual penguin identification. The dataset and source code are available here: https://033labcodes.github.io/igrass24_penguin/ Comment: Accepted by 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2024)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.14146 Zobrazit plný text záznamu View this record from Arxiv