Quantitative evaluation of atherosclerotic plaque phantom by near-infrared multispectral imaging with three wavelengths

2014 ◽  
Author(s):  
Ryo Nagao ◽  
Katsunori Ishii ◽  
Kunio Awazu
Keyword(s):  
Quantitative Evaluation ◽  
Atherosclerotic Plaque ◽  
Near Infrared ◽  
Multispectral Imaging

Optimal wavelengths for near-infrared multispectral imaging of atherosclerotic plaque

2015 ◽  
Vol 22 (2) ◽  
pp. 329-334 ◽  
Author(s):  
Ryo Nagao ◽  
Katsunori Ishii ◽  
Kunio Awazu
Keyword(s):  
Atherosclerotic Plaque ◽  
Near Infrared ◽  
Multispectral Imaging

scholarly journals Rapid identification of shallow inundation for mosquito disease mitigation using drone-derived multispectral imagery

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Tasya Vadya Sarira ◽  
Kenneth Clarke ◽  
Philip Weinstein ◽  
Lian Pin Koh ◽  
Megan Lewis
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
South Australia ◽  
Rapid Identification ◽  
Breeding Habitat ◽  
Mapping Accuracy ◽  
Normalised Difference Vegetation Index ◽  
Mosquito Breeding

Mosquito breeding habitat identification often relies on slow, labour-intensive and expensive ground surveys. With advances in remote sensing and autonomous flight technologies, we endeavoured to accelerate this detection by assessing the effectiveness of a drone multispectral imaging system to determine areas of shallow inundation in an intertidal saltmarsh in South Australia. Through laboratory experiments, we characterised Near-Infrared (NIR) reflectance responses to water depth and vegetation cover, and established a reflectance threshold for mapping water sufficiently deep for potential mosquito breeding. We then applied this threshold to field-acquired drone imagery and used simultaneous in-situ observations to assess its mapping accuracy. A NIR reflectance threshold of 0.2 combined with a vegetation mask derived from Normalised Difference Vegetation Index (NDVI) resulted in a mapping accuracy of 80.3% with a Cohen’s Kappa of 0.5, with confusion between vegetation and shallow water depths (< 10 cm) appearing to be major causes of error. This high degree of mapping accuracy was achieved with affordable drone equipment, and commercially available sensors and Geographic Information Systems (GIS) software, demonstrating the efficiency of such an approach to identify shallow inundation likely to be suitable for mosquito breeding.

Sign in / Sign up

Export Citation Format

Share Document