scholarly journals Incorporating Surface Elevation Information in UAV Multispectral Images for Mapping Weed Patches

2018 ◽  
Vol 4 (11) ◽  
pp. 132 ◽  
Author(s):  
Theodota Zisi ◽  
Thomas Alexandridis ◽  
Spyridon Kaplanis ◽  
Ioannis Navrozidis ◽  
Afroditi-Alexandra Tamouridou ◽  
...  
Keyword(s):  
Plant Height ◽  
Near Infrared ◽  
Surface Model ◽  
Digital Surface Model ◽  
Multispectral Images ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Vegetation Height ◽  
Weed Distribution ◽  
Terrain Elevation

Accurate mapping of weed distribution within a field is a first step towards effective weed management. The aim of this work was to improve the mapping of milk thistle (Silybum marianum) weed patches through unmanned aerial vehicle (UAV) images using auxiliary layers of information, such as spatial texture and estimated vegetation height from the UAV digital surface model. UAV multispectral images acquired in the visible and near-infrared parts of the spectrum were used as the main source of data, together with texture that was estimated for the image bands using a local variance filter. The digital surface model was created from structure from motion algorithms using the UAV image stereopairs. From this layer, the terrain elevation was estimated using a focal minimum filter followed by a low-pass filter. The plant height was computed by subtracting the terrain elevation from the digital surface model. Three classification algorithms (maximum likelihood, minimum distance and an object-based image classifier) were used to identify S. marianum from other vegetation using various combinations of inputs: image bands, texture and plant height. The resulting weed distribution maps were evaluated for their accuracy using field-surveyed data. Both texture and plant height have helped improve the accuracy of classification of S. marianum weed, increasing the overall accuracy of classification from 70% to 87% in 2015, and from 82% to 95% in 2016. Thus, as texture is easier to compute than plant height from a digital surface model, it may be preferable to be used in future weed mapping applications.

scholarly journals TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 161-169 ◽  
Author(s):  
K. Bakuła ◽  
P. Kupidura ◽  
Ł. Jełowicki
Keyword(s):  
Land Cover ◽  
Laser Scanning ◽  
Near Infrared ◽  
Airborne Laser Scanning ◽  
Surface Model ◽  
Digital Surface Model ◽  
Airborne Laser ◽  
Laser Reflectance ◽  
Elevation Data ◽  
Intensity Images

Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.

scholarly journals TESTING OF LAND COVER CLASSIFICATION FROM MULTISPECTRAL AIRBORNE LASER SCANNING DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 161-169 ◽  
Author(s):  
K. Bakuła ◽  
P. Kupidura ◽  
Ł. Jełowicki
Keyword(s):  
Land Cover ◽  
Laser Scanning ◽  
Near Infrared ◽  
Airborne Laser Scanning ◽  
Surface Model ◽  
Digital Surface Model ◽  
Airborne Laser ◽  
Laser Reflectance ◽  
Elevation Data ◽  
Intensity Images

Multispectral Airborne Laser Scanning provides a new opportunity for airborne data collection. It provides high-density topographic surveying and is also a useful tool for land cover mapping. Use of a minimum of three intensity images from a multiwavelength laser scanner and 3D information included in the digital surface model has the potential for land cover/use classification and a discussion about the application of this type of data in land cover/use mapping has recently begun. In the test study, three laser reflectance intensity images (orthogonalized point cloud) acquired in green, near-infrared and short-wave infrared bands, together with a digital surface model, were used in land cover/use classification where six classes were distinguished: water, sand and gravel, concrete and asphalt, low vegetation, trees and buildings. In the tested methods, different approaches for classification were applied: spectral (based only on laser reflectance intensity images), spectral with elevation data as additional input data, and spectro-textural, using morphological granulometry as a method of texture analysis of both types of data: spectral images and the digital surface model. The method of generating the intensity raster was also tested in the experiment. Reference data were created based on visual interpretation of ALS data and traditional optical aerial and satellite images. The results have shown that multispectral ALS data are unlike typical multispectral optical images, and they have a major potential for land cover/use classification. An overall accuracy of classification over 90% was achieved. The fusion of multi-wavelength laser intensity images and elevation data, with the additional use of textural information derived from granulometric analysis of images, helped to improve the accuracy of classification significantly. The method of interpolation for the intensity raster was not very helpful, and using intensity rasters with both first and last return numbers slightly improved the results.

scholarly journals Management Recommendation Generation for Areas Under Forest Restoration Process through Images Obtained by UAV and LiDAR

2019 ◽  
Vol 11 (13) ◽  
pp. 1508
Author(s):  
Bruna Paolinelli Reis ◽  
Sebastião Venâncio Martins ◽  
Elpídio Inácio Fernandes Filho ◽  
Tathiane Santi Sarcinelli ◽  
José Marinaldo Gleriani ◽  
...  
Keyword(s):  
Expert System ◽  
Land Cover ◽  
Forest Restoration ◽  
Near Infrared ◽  
Control Plant ◽  
Surface Model ◽  
Digital Surface Model ◽  
Monitoring Methods ◽  
Restoration Process ◽  
Management Recommendations

Evaluating and monitoring forest areas during a restoration process is indispensable to estimate the success or failure of management intervention and to correct the restoration trajectory through adaptive management. However, the field measurement of several indicators in large areas can be expensive and laborious, and establishing reference values for indicators is difficult. The use of supervised classification techniques of high resolution images, combined with an expert system to generate management recommendations, can be considered promising tools for monitoring and evaluating restoration areas. The objective of the present study was to elaborate an expert system of management recommendation generation for areas under restoration, which were monitored by two different remote sensors: UAV (Unmanned Aerial Vehicle) and LiDAR (Light Detection and Ranging). The study was carried out in areas under restoration with about 54 ha and five years of implementation, owned by Fibria Celulose S.A. (recently acquired by Suzano S.A.), in the southern region of Bahia State, Brazil. We used images from Canon S110 NIR (green, red, near infrared) on UAV and LiDAR data compositions (intensity image, digital surface model, digital terrain model, normalized digital surface model). The monitored restoration indicator entailed land cover separated into three classes: Canopy cover, bare soil and grass cover. The images were classified using the Random Forest (RF) and Maximum Likelihood (ML) algorithms and the area occupied by each land cover classes was calculated. An expert system was developed in ArcGIS to define management recommendations according to the land cover classes, and then we compared the recommendations generated by both algorithms and images. There was a slight difference between the recommendations generated by the different combinations of images and classifiers. The most frequent management recommendation was “weed control + plant seedlings” (34%) for all evaluated methods. The image monitoring methods suggested by this study proved to be efficient, mainly by reducing the time and cost necessary for field monitoring and increasing the accuracy of the generated management recommendations.

A PRACTICAL NONINVASIVE BLOOD GLUCOSE MEASUREMENT SYSTEM USING NEAR-INFRARED SENSORS

2021 ◽  
pp. 2150040
Author(s):  
Reza Taghizadeh-Behbahani ◽  
Reza Boostani ◽  
Mehran Yazdi
Keyword(s):  
Blood Glucose ◽  
Glucose Level ◽  
Gold Standard ◽  
Near Infrared ◽  
Glucose Measurement ◽  
Diabetic Patients ◽  
Standard Equipment ◽  
Pass Filter ◽  
Acceptable Error ◽  
Low Pass Filter

Due to the drastic fluctuation of glucose level in diabetic patients throughout a day, especially when they take a sweet breakfast, and greasy lunch and dinner, their insulin must be promptly measured invasively after each meal. However, invasive measurement of the blood glucose level (BGL) is painful and might lead to infection. In this paper, we design a hardware–software system for estimating BGL in a real-time manner, according to the amount of near-infrared (NIR) absorption at 950 nm. The received light wave is amplified and passed through a low-pass filter (LPF) to eliminate the irrelevant frequency components. We have executed our experiment according to three scenarios: (i) Certain amount of glucose is dissolved in water and the glucose concentration is measured by our equipment, (ii) 5-cm3 blood sample of participants is taken and the BGL is measured in a test tube by our apparatus and then this sample is moved to the gold-standard equipment and the actual value of BGL is measured. The measured voltage versus BGL in our designed optical equipment indicates a fairly linear relation between them. (iii) BGL is measured over the fingertip and compared to the gold-standard value. In this case, the measurement error does not exceed 25% and the voltage change with respect to the BGL shows a nonlinear behavior. The designed system has a low cost and provides an acceptable error, making it suitable to be commercialized for diabetic patients.

Design of Programmable Wideband Low Pass Filter with Continuous-Time/Discrete-Time Hybrid Architecture

2017 ◽  
Vol E100.C (10) ◽  
pp. 858-865 ◽  
Author(s):  
Yohei MORISHITA ◽  
Koichi MIZUNO ◽  
Junji SATO ◽  
Koji TAKINAMI ◽  
Kazuaki TAKAHASHI
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Hybrid Architecture ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass
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