Diamond Nanoparticle with Cross Aperture for Improving Absorbance Characteristics of Multispectral Sensors

2015 ◽  
Author(s):  
Erdem Aslan ◽  
Ekin Aslan ◽  
Sabri Kaya ◽  
O. Galip Saracoglu ◽  
Mustafa Turkmen
Keyword(s):  
Multispectral Sensors ◽  
Diamond Nanoparticle

scholarly journals Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

2021 ◽  
Vol 13 (4) ◽  
pp. 830
Author(s):  
Adam R. Benjamin ◽  
Amr Abd-Elrahman ◽  
Lyn A. Gettys ◽  
Hartwig H. Hochmair ◽  
Kyle Thayer
Keyword(s):  
Image Analysis ◽  
Aquatic Vegetation ◽  
Accuracy Assessment ◽  
Unmanned Aerial Systems ◽  
Temporal Area ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Significant Difference ◽  
Additional Processing ◽  
Multispectral Sensors

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

scholarly journals Testing the spectral resolutions of the new multispectral sensors for detecting Phaeosphaeria leaf spot (PLS) infestations in maize crop

2018 ◽  
Vol 7 (1) ◽  
pp. 1 ◽  
Author(s):  
John Odindi ◽  
Elhadi Adam ◽  
Elfatih M. Abdel-Rahman ◽  
Onisimo Mutanga
Keyword(s):  
Leaf Spot ◽  
Maize Crop ◽  
Multispectral Sensors

Subpixel temperature retrieval with multispectral sensors

1999 ◽  
Author(s):  
John J. Szymanski ◽  
Christoph C. Borel ◽  
Quincy O. Harberger ◽  
Pawel Smolarkiewicz ◽  
James P. Theiler
Keyword(s):  
Temperature Retrieval ◽  
Multispectral Sensors

scholarly journals USING PASSIVE AND ACTIVE MULTISPECTRAL SENSORS ON THE CORRELATION WITH THE PHENOLOGICAL INDICES OF COTTON

2017 ◽  
Vol 37 (4) ◽  
pp. 782-789 ◽  
Author(s):  
Heloisa B. Souza ◽  
Fabio H. R. Baio ◽  
Danilo C. Neves
Keyword(s):  
Multispectral Sensors

Cuckoo Search Based Decision Fusion Techniques for Natural Terrain Understanding

2019 ◽  
pp. 813-836
Author(s):  
Arpita Sharma ◽  
Samiksha Goel
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Cuckoo Search ◽  
Decision Fusion ◽  
Majority Voting ◽  
Decision Level ◽  
Natural Terrain ◽  
Terrain Features ◽  
Level Fusion ◽  
Multispectral Sensors

This paper proposes two novel nature inspired decision level fusion techniques, Cuckoo Search Decision Fusion (CSDF) and Improved Cuckoo Search Decision Fusion (ICSDF) for enhanced and refined extraction of terrain features from remote sensing data. The developed techniques derive their basis from a recently introduced bio-inspired meta-heuristic Cuckoo Search and modify it suitably to be used as a fusion technique. The algorithms are validated on remote sensing satellite images acquired by multispectral sensors namely LISS3 Sensor image of Alwar region in Rajasthan, India and LANDSAT Sensor image of Delhi region, India. Overall accuracies obtained are substantially better than those of the four individual terrain classifiers used for fusion. Results are also compared with majority voting and average weighing policy fusion strategies. A notable achievement of the proposed fusion techniques is that the two difficult to identify terrains namely barren and urban are identified with similar high accuracies as other well identified land cover types, which was not possible by single analyzers.

Design and fabrication of a two-phase diamond nanoparticle aided fast PCR device

2019 ◽  
Vol 1068 ◽  
pp. 28-40 ◽  
Author(s):  
Masoud Madadelahi ◽  
Erfan Ghazimirsaeed ◽  
Amir Shamloo
Keyword(s):  
Two Phase ◽  
Diamond Nanoparticle

scholarly journals A Comparative Study of RGB and Multispectral Sensor-Based Cotton Canopy Cover Modelling Using Multi-Temporal UAS Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2757 ◽  
Author(s):  
Akash Ashapure ◽  
Jinha Jung ◽  
Anjin Chang ◽  
Sungchan Oh ◽  
Murilo Maeda ◽  
...  
Keyword(s):  
Comparative Study ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Canopy Cover ◽  
Unmanned Aircraft ◽  
Estimation Methods ◽  
Green Vegetation ◽  
Multi Temporal ◽  
Rgb Images ◽  
Multispectral Sensors

This study presents a comparative study of multispectral and RGB (red, green, and blue) sensor-based cotton canopy cover modelling using multi-temporal unmanned aircraft systems (UAS) imagery. Additionally, a canopy cover model using an RGB sensor is proposed that combines an RGB-based vegetation index with morphological closing. The field experiment was established in 2017 and 2018, where the whole study area was divided into approximately 1 x 1 m size grids. Grid-wise percentage canopy cover was computed using both RGB and multispectral sensors over multiple flights during the growing season of the cotton crop. Initially, the normalized difference vegetation index (NDVI)-based canopy cover was estimated, and this was used as a reference for the comparison with RGB-based canopy cover estimations. To test the maximum achievable performance of RGB-based canopy cover estimation, a pixel-wise classification method was implemented. Later, four RGB-based canopy cover estimation methods were implemented using RGB images, namely Canopeo, the excessive greenness index, the modified red green vegetation index and the red green blue vegetation index. The performance of RGB-based canopy cover estimation was evaluated using NDVI-based canopy cover estimation. The multispectral sensor-based canopy cover model was considered to be a more stable and accurately estimating canopy cover model, whereas the RGB-based canopy cover model was very unstable and failed to identify canopy when cotton leaves changed color after canopy maturation. The application of a morphological closing operation after the thresholding significantly improved the RGB-based canopy cover modeling. The red green blue vegetation index turned out to be the most efficient vegetation index to extract canopy cover with very low average root mean square error (2.94% for the 2017 dataset and 2.82% for the 2018 dataset), with respect to multispectral sensor-based canopy cover estimation. The proposed canopy cover model provides an affordable alternate of the multispectral sensors which are more sensitive and expensive.

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