Coastal observation using new hyperspectral imager for UAVs

التفاصيل البيبلوغرافية
العنوان: Coastal observation using new hyperspectral imager for UAVs
المؤلفون: Yukio Kosugi, Haruyuki Seki, Genya Saito, Kuniaki Uto, Teruhisa Komatsu
المصدر: IGARSS
بيانات النشر: IEEE, 2017.
سنة النشر: 2017
مصطلحات موضوعية: coastal observation, hyperspectral imager, Hyperspectral imaging, UAV, 0208 environmental biotechnology, Spectral response, marine macrophytes, image matching, sand, 02 engineering and technology, 01 natural sciences, Optical imaging, Ecosystems, 010309 optics, remote sensing, Optical transmitters, Altitude, Japan, coastal area, Sea measurements, 0103 physical sciences, underwater measurement, aerial measurement, remotely sensed hyperspectral images, Underwater, Spectral resolution, Image resolution, Remote sensing, gelidium, East coast, Cross-correlation, sea surface reflection, 020801 environmental engineering, geophysical image processing, Earth ecosystem, Mirrors, pattern matching, autonomous aerial vehicles, high-performance UAV, eastern Izu Oshima coast, Geology, image classification
الوصف: Observation of coastal area is important for preserving Earth's ecosystem. In this paper, we introduce a customizable, low-cost whiskbroom hyperspectral imager (bands: 288, spectral response range: 340–820, spectral resolution: 15 nm) for UAVs. A newly developed high-performance UAV is capable of landing on the water, so that underwater measurement without sea surface reflection is possible. We investigate the characteristics of remotely sensed hyperspectral images of the east coast of the Izu Oshima, Japan. In an aerial measurement of a coastal area from a 20-m altitude, hyperspectral images with a 0.5-m spatial resolution and an 8-m swath were acquired. Gelidium and sand were classified by pattern matching using cross correlation.
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39e4f14cfbe0813658166c483ba670f1
https://doi.org/10.1109/igarss.2017.8127781
رقم الأكسشن: edsair.doi.dedup.....39e4f14cfbe0813658166c483ba670f1
قاعدة البيانات: OpenAIRE