Angioscopic image-enhanced observation of atherosclerotic plaque phantom by near-infrared multispectral imaging at wavelengths around 1200 nm

2015 ◽  
Author(s):  
K. Ishii ◽  
R. Nagao ◽  
D. Matsui ◽  
K. Awazu
Keyword(s):  
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.

scholarly journals Near-infrared Spectroscopy and Hyperspectral Imaging for Sugar Content Evaluation in Potatoes over Multiple Growing Seasons

2020 ◽  
Author(s):  
Ahmed M Rady ◽  
Daniel E Guyer ◽  
Nicholas J Watson
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Sucrose Concentration ◽  
Sugar Content ◽  
Imaging Systems ◽  
Potato Tubers ◽  
Forward Selection ◽  
Concentration Data ◽  
Growing Seasons

Abstract Sugar content is one of the most important properties of potato tubers as it directly affects their processing and the final product quality, especially for fried products. In this study, data obtained from spectroscopic (interactance and reflectance) and hyperspectral imaging systems were used individually or fused to develop non-cultivar nor growing season-specific regression and classification models for potato tubers based on glucose and sucrose concentration. Data was acquired over three growing seasons for two potato cultivars. The most influential wavelengths were selected from the imaging systems using interval partial least squares for regression and sequential forward selection for classification. Hyperspectral imaging showed the highest regression performance for glucose with a correlation coefficient (ratio of performance to deviation) or r(RPD) of 91.8(2.41) which increased to 94%(2.91) when the data was fused with the interactance data. The sucrose regression results had the highest accuracy using data obtained from the interactance system with r(RPD) values of 74.5%(1.40) that increased to 84.4%(1.82) when the data was fused with the reflectance data. Classification was performed to identify tubers with either high or low sugar content. Classification performance showed accuracy values as high as 95% for glucose and 80.1% for sucrose using hyperspectral imaging, with no noticeable improvement when data was fused from the other spectroscopic systems. When testing the robustness of the developed models over different seasons, it was found that the regression models had r(RPD) values of 55(1.19)–90.3%(2.34) for glucose and 35.8(1.07)–82.2%(1.29) for sucrose. Results obtained in this study demonstrate the feasibility of developing a rapid monitoring system using multispectral imaging and data fusion methods for online evaluation of potato sugar content.

Non-destructively sensing pork quality using near infrared multispectral imaging technique

RSC Advances ◽  
2015 ◽  
Vol 5 (116) ◽  
pp. 95903-95910 ◽  
Author(s):  
Qiping Huang ◽  
Huanhuan Li ◽  
Jiewen Zhao ◽  
Gengping Huang ◽  
Quansheng Chen
Keyword(s):  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
Imaging Technique ◽  
Water Holding Capacity ◽  
Pork Quality ◽  
Water Holding ◽  
Non Destructive

Near infrared multispectral imaging system based on three wavebands—1280 nm, 1440 nm and 1660 nm—was developed for the non-destructive sensing of the tenderness and water holding capacity of pork.

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