scholarly journals THE EFFECTIVE OF DIFFERENT EXCITATION WAVELENGTHS ON THE IDENTIFICATION OF PLANT SPECIES BASED ON FLUORESCENCE LIDAR

2016 ◽  
Vol XLI-B1 ◽  
pp. 147-150
Author(s):  
Jian Yang ◽  
Wei Gong ◽  
Shuo Shi ◽  
Lin Du ◽  
Jia Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Fluorescence Spectra ◽  
Principal Component ◽  
Laser Induced Fluorescence ◽  
Support Vector ◽  
Excitation Wavelength ◽  
Lidar System ◽  
Excitation Light

Laser-induced fluorescence (LIF) served as an active technology has been widely used in many field, and it is closely related to excitation wavelength (EW). The objective of this investigation is to discuss the performance of different EWs of LIF LiDAR in identifying plant species. In this study, the 355, 460 and 556 nm lasers were utilized to excite the leaf fluorescence and the fluorescence spectra were measured by using the LIF LiDAR system built in the laboratory. Subsequently, the principal component analysis (PCA) with the help of support vector machine (SVM) was utilized to analyse fluorescence spectra. For the three EWs, the overall identification rates of the six plant species were 80 %, 83.3 % and 90 %. Experimental results demonstrated that 556 nm excitation light source is superior to 355 and 460 nm for the classification of the plant species for the same genus in this study. Thus, an appropriate excitation wavelength should be considered when the LIF LiDAR was utilized in the field of remote sensing based on the LIF technology.

scholarly journals THE EFFECTIVE OF DIFFERENT EXCITATION WAVELENGTHS ON THE IDENTIFICATION OF PLANT SPECIES BASED ON FLUORESCENCE LIDAR

2016 ◽  
Vol XLI-B1 ◽  
pp. 147-150 ◽  
Author(s):  
Jian Yang ◽  
Wei Gong ◽  
Shuo Shi ◽  
Lin Du ◽  
Jia Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Fluorescence Spectra ◽  
Principal Component ◽  
Laser Induced Fluorescence ◽  
Support Vector ◽  
Excitation Wavelength ◽  
Lidar System ◽  
Excitation Light

Laser-induced fluorescence (LIF) served as an active technology has been widely used in many field, and it is closely related to excitation wavelength (EW). The objective of this investigation is to discuss the performance of different EWs of LIF LiDAR in identifying plant species. In this study, the 355, 460 and 556 nm lasers were utilized to excite the leaf fluorescence and the fluorescence spectra were measured by using the LIF LiDAR system built in the laboratory. Subsequently, the principal component analysis (PCA) with the help of support vector machine (SVM) was utilized to analyse fluorescence spectra. For the three EWs, the overall identification rates of the six plant species were 80 %, 83.3 % and 90 %. Experimental results demonstrated that 556 nm excitation light source is superior to 355 and 460 nm for the classification of the plant species for the same genus in this study. Thus, an appropriate excitation wavelength should be considered when the LIF LiDAR was utilized in the field of remote sensing based on the LIF technology.

scholarly journals Mobile lidar system for environmental monitoring

2018 ◽  
Vol 176 ◽  
pp. 07002
Author(s):  
Guangyu Zhao ◽  
Ming Lian ◽  
Yiyun Li ◽  
Zheng Duan ◽  
Shiming Zhu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Monitoring ◽  
Nd:Yag Laser ◽  
Field Experiments ◽  
Laser Induced Fluorescence ◽  
Dye Laser ◽  
Lidar System ◽  
Sensing System ◽  
Atomic Mercury

A versatile mobile remote sensing system for multidisciplinary environmental monitoring tasks on the Chinese scene is described. The system includes a 20 Hz Nd:YAG laser-pumped dye laser, optical transmitting/receiving systems with a 30 cm and a 40 cm Newtonian telescope, and electronics, all integrated in a laboratory, installed on a Jiefang truck. Results from field experiments on atomic mercury DIAL mapping and remote laser-induced fluorescence and break-down spectroscopy are given.

scholarly journals Remote Laser Induced Fluorescence of Soils and Rocks

Photonics ◽  
2021 ◽  
Vol 8 (10) ◽  
pp. 411
Author(s):  
Vasily N. Lednev ◽  
Alexey F. Bunkin ◽  
Sergey M. Pershin ◽  
Mikhail Ya. Grishin ◽  
Diana G. Artemova ◽  
...  
Keyword(s):  
Fluorescence Spectroscopy ◽  
Fluorescence Spectra ◽  
Space Exploration ◽  
Laser Induced Fluorescence ◽  
Excitation Wavelength ◽  
Extraterrestrial Life ◽  
Future Space ◽  
Lidar Sensing ◽  
First Time ◽  
Different Soils

The laser induced fluorescence spectroscopy was systematically utilized for remote sensing of different soils and rocks for the first time, to the best of our knowledge. Laser induced fluorescence spectroscopy measurements were carried out by the developed nanosecond LIDAR instrument with variable excitation wavelength (355, 532 and 1064 nm). LIDAR sensing of different Brazil soil samples have been carried out in order to construct a spectral database. The laser induced fluorescence spectra interpretation for different samples has been discussed in detail. The perspectives of LIDAR sensing of organic samples deposited at soils and rock have been discussed including future space exploration missions in the search for extraterrestrial life.

scholarly journals Experimental analysis on the optimal excitation wavelength for fine-grained identification of refined oil pollutants on water surface based on laser-induced fluorescence

2021 ◽  
Author(s):  
Ming Xie ◽  
Yunpeng Jia ◽  
Ying Li ◽  
Xiaohua Cai ◽  
Kai Cao
Keyword(s):  
Oil Spill ◽  
Theoretical Basis ◽  
Water Surface ◽  
Laser Induced Fluorescence ◽  
Excitation Wavelength ◽  
Refined Oil ◽  
Fine Grained ◽  
Optimal Excitation ◽  
Oil Spill Identification

Abstract Laser-induced fluorescence (LIF) is an effective, all-weather oil spill identification method that has been widely applied for oil spill monitoring. However, the distinguishability on oil types is seldom considered while selecting excitation wavelength. This study is intended to find the optimal excitation wavelength for fine-grained classification of refined oil pollutants using LIF by comparing the distinguishability of fluorometric spectra under various excitation wavelengths on some typical types of refined-oil samples. The results show that the fluorometric spectra of oil samples significantly vary under different excitation wavelengths, and the four types of oil applied in this study are most likely to be distinguished under the excitation wavelengths of 395 nm and 420 nm. This study is expected to improve the ability of oil types identification using LIF method without increasing time or other cost, and also provides theoretical basis for the development of portable LIF devices for oil spill identification.

Biomimetic Pattern Recognition for Classification of Proteomic Profile

2013 ◽  
Vol 791-793 ◽  
pp. 1961-1964
Author(s):  
Xiao Li Yang ◽  
Qiong He
Keyword(s):  
Pattern Recognition ◽  
Cancer Diagnosis ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Proteomic Profile ◽  
Satisfactory Model ◽  
Over Time

We propose a biomimetic pattern recognition (BPR) approach for classification of proteomic profile. The proposed approach preprocess profile using iterative minimum in adaptive setting window (IMASW) method for baseline correction, discrete wavelet transform (DWT) for fitting and smoothing, and average total ion normalization (ATIN) for remove the influence of vary amount of sample and degradation over time. Then principal component analysis (PCA) and BPR build classification model. With an optimization of the parameters involved in the modeling, we obtain a satisfactory model for cancer diagnosis in three proteomic profile datasets. The predicted results show that BPR technique is more reliable and efficient than support vector machine (SVM) method.

A graph-based active learning method for classification of remote sensing images

2018 ◽  
Vol 16 (04) ◽  
pp. 1850023
Author(s):  
Lian-Zhi Huo ◽  
Ping Tang
Keyword(s):  
Remote Sensing ◽  
Graph Theory ◽  
Active Learning ◽  
Image Classification ◽  
Selection Procedure ◽  
Experimental Tests ◽  
Maximization Problem ◽  
Support Vector ◽  
Training Samples

Remote sensing (RS) technology provides essential data for monitoring the Earth. To fully utilize the data, image classification is often needed to convert data to information. The success of image classification methods greatly depends on the quality and quantity of training samples. To effectively select more informative training samples, this paper proposes a new active learning (AL) technique for classification of remote sensing (RS) images based on graph theory. A new diversity criterion is proposed based on geometrical features of the support vector machines (SVM) outputs. The diversity selection procedure is converted to the densest k-subgraph [Formula: see text] maximization problem in graph theory. The [Formula: see text] maximization problem is solved by a greedy algorithm. The proposed technique is compared with competing methods adopted in RS community. Experimental tests are performed on very high resolution (VHR) multispectral and hyperspectral images. Experimental results demonstrate that the proposed technique leads to comparable or even better classification accuracies with respect to competing methods on the two datasets.

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