Soil nutrient estimation and mapping in agriculture land based on improved ELM and UAV imaging spectrometry

2021 ◽  
Author(s):  
Nisha Bao ◽  
Xiaoyu Yang ◽  
Yue Cao
Keyword(s):  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Feature Selection Method ◽  
Soil Nutrient ◽  
Pso Algorithm ◽  
Correction Method ◽  
Successive Projections Algorithm ◽  
Imaging Spectrometry ◽  
Rapid Acquisition ◽  
Standard Normal Variate

<p>Soil nutrient is one of the most important properties to support farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. The goal of this study was to explore the preprocessing and modeling method of hyperspectral image acquired from UAV platform for soil organic matter (SOM) and soil total nitrogen (STN) content estimation in farmland. The results showed that: 1) Multiple Scattering Correction method performed better in reducing image scattering noise rather than Standard Normal Variate transformation or spectral derivatives with higher correlation and lower signal-to-noise ratio; 2) The proposed feature selection method, which was combined with Competitive Adaptive Reweighted Sampling algorithm (CARS) and Successive Projections Algorithm (SPA), could provide selective preference for hyperspectral bands with final 24 feature bands for SOM estimation and 22 feature bands for STN estimation; 3) The particle swarm optimization (PSO) algorithm was selected to optimize input weights and hidden biases of extreme learning machine (ELM)  model for SOM and STN prediction. The PSO-ELM model with input selective preference bands produced higher prediction accuracy with the R<sup>2</sup> of 0.73, RPD of 1.91 for SOM and R<sup>2</sup> of 0.63, RPD of 1.53 for STN respectively rather than ELM model. These outcomes provided a technical support for wider application of soil properties estimation using imaging spectrometry in agriculture precision monitoring and mapping.</p>

scholarly journals Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3919
Author(s):  
Xiaoyu Yang ◽  
Nisha Bao ◽  
Wenwen Li ◽  
Shanjun Liu ◽  
Yanhua Fu ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Signal To Noise Ratio ◽  
Feature Selection Method ◽  
Soil Nutrient ◽  
Pso Algorithm ◽  
Support Vector ◽  
Least Squares Regression ◽  
Successive Projections Algorithm ◽  
Imaging Spectrometry ◽  
Rapid Acquisition

Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R2 of 0.73 and RPD of 1.91 for SOM, R2 of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry.

scholarly journals Machine Learning-Based Prediction of Selected Parameters of Commercial Biomass Pellets Using Line Scan Near Infrared-Hyperspectral Image

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 316
Author(s):  
Lakkana Pitak ◽  
Kittipong Laloon ◽  
Seree Wongpichet ◽  
Panmanas Sirisomboon ◽  
Jetsada Posom
Keyword(s):  
Near Infrared ◽  
Hyperspectral Image ◽  
Thermal Conversion ◽  
High Energy ◽  
Volatile Matter ◽  
Successive Projections Algorithm ◽  
Color Distribution ◽  
Standard Normal Variate ◽  
Distribution Mapping ◽  
Standard Normal

Biomass pellets are required as a source of energy because of their abundant and high energy. The rapid measurement of pellets is used to control the biomass quality during the production process. The objective of this work was to use near infrared (NIR) hyperspectral images for predicting the properties, i.e., fuel ratio (FR), volatile matter (VM), fixed carbon (FC), and ash content (A), of commercial biomass pellets. Models were developed using either full spectra or different spatial wavelengths, i.e., interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA), wavelengths and different spectral preprocessing techniques. Their performances were then compared. The optimal model for predicting FR could be created with second derivative (D2) spectra with iSPA-100 wavelengths, while VM, FC, and A could be predicted using standard normal variate (SNV) spectra with iSPA-100 wavelengths. The models for predicting FR, VM, FC, and A provided R2 values of 0.75, 0.81, 0.82, and 0.87, respectively. Finally, the prediction of the biomass pellets’ properties under color distribution mapping was able to track pellet quality to control and monitor quality during the operation of the thermal conversion process and can be intuitively used for applications with screening.

Median Morphological Filter Design Based on the PSO Algorithm

2011 ◽  
Vol 128-129 ◽  
pp. 181-184
Author(s):  
You Lian Zhu ◽  
Cheng Huang
Keyword(s):  
Filter Design ◽  
Design Method ◽  
Signal To Noise Ratio ◽  
Pso Algorithm ◽  
Morphological Operations ◽  
Morphological Filter ◽  
Signal To Noise ◽  
Noise Ratio ◽  
The Cost ◽  
Filter Design Method

Design of morphological filter greatly depends on morphological operations and structuring elements selection. A filter design method used median closing morphological operation is proposed to enhance the image denoising ability and the PSO algorithm is introduced for structural elements selecting. The method takes the peak value signal-to-noise ratio (PSNR) as the cost function and may adaptively build unit structuring elements with zero square matrix. Experimental results show the proposed method can effectively remove impulse noise from a noisy image, especially from a low signal-to-noise ratio (SNR) image; the noise reduction performance has obvious advantages than the other.

scholarly journals Automotive antenna diversity system for satellite radio with high phase accuracy in low SNR-scenarios

2018 ◽  
Vol 10 (5-6) ◽  
pp. 578-586 ◽  
Author(s):  
Simon Senega ◽  
Ali Nassar ◽  
Stefan Lindenmeier
Keyword(s):  
Signal To Noise Ratio ◽  
A Priori ◽  
Multipath Fading ◽  
Correction Method ◽  
Antenna System ◽  
Antenna Diversity ◽  
Phase Detection ◽  
Low Snr ◽  
Single Antenna ◽  
Noise Correction

AbstractFor a fast scan-phase satellite radio antenna diversity system a noise correction method is presented for a significant improvement of audio availability at low signal-to-noise ratio (SNR) conditions. An error analysis of the level and phase detection within the diversity system in the presence of noise leads to a correction method based on a priori knowledge of the system's noise floor. This method is described and applied in a hardware example of a satellite digital audio radio services antenna diversity circuit for fast fading conditions. Test drives, which have been performed in real fading scenarios, are described and results are analyzed statistically. Simulations of the scan-phase antenna diversity system show higher signal amplitudes and availabilities. Measurement results of dislocated antennas as well as of a diversity antenna set on a single mounting position are presented. A comparison of a diversity system with noise correction, the same system without noise correction, and a single antenna system with each other is performed. Using this new method in fast multipath fading driving scenarios underneath dense foliage with a low SNR of the antenna signals, a reduction in audio mute time by one order of magnitude compared with single antenna systems is achieved with the diversity system.

scholarly journals Quality Metrics Evaluation of Hyperspectral Images

2014 ◽  
Vol XL-8 ◽  
pp. 1221-1226 ◽  
Author(s):  
A. K. Singh ◽  
H. V. Kumar ◽  
G. R. Kadambi ◽  
J. K. Kishore ◽  
J. Shuttleworth ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Quality Parameters ◽  
Quality Metrics ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Error Ratio ◽  
Classified Image

In this paper, the quality metrics evaluation on hyperspectral images has been presented using k-means clustering and segmentation. After classification the assessment of similarity between original image and classified image is achieved by measurements of image quality parameters. Experiments were carried out on four different types of hyperspectral images. Aerial and spaceborne hyperspectral images with different spectral and geometric resolutions were considered for quality metrics evaluation. Principal Component Analysis (PCA) has been applied to reduce the dimensionality of hyperspectral data. PCA was ultimately used for reducing the number of effective variables resulting in reduced complexity in processing. In case of ordinary images a human viewer plays an important role in quality evaluation. Hyperspectral data are generally processed by automatic algorithms and hence cannot be viewed directly by human viewers. Therefore evaluating quality of classified image becomes even more significant. An elaborate comparison is made between k-means clustering and segmentation for all the images by taking Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), Maximum Squared Error, ratio of squared norms called L2RAT and Entropy. First four parameters are calculated by comparing the quality of original hyperspectral image and classified image. Entropy is a measure of uncertainty or randomness which is calculated for classified image. Proposed methodology can be used for assessing the performance of any hyperspectral image classification techniques.

scholarly journals 3D MPRAGE improves classification of cortical lesions in multiple sclerosis

2008 ◽  
Vol 14 (9) ◽  
pp. 1214-1219 ◽  
Author(s):  
F Nelson ◽  
A Poonawalla ◽  
P Hou ◽  
JS Wolinsky ◽  
PA Narayana
Keyword(s):  
Multiple Sclerosis ◽  
White Matter ◽  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Inversion Recovery ◽  
High Signal ◽  
Cortical Lesions ◽  
Rapid Acquisition ◽  
Lesion Morphology

Background Gray matter lesions are known to be common in multiple sclerosis (MS) and are suspected to play an important role in disease progression and clinical disability. A combination of magnetic resonance imaging (MRI) techniques, double-inversion recovery (DIR), and phase-sensitive inversion recovery (PSIR), has been used for detection and classification of cortical lesions. This study shows that high-resolution three-dimensional (3D) magnetization-prepared rapid acquisition with gradient echo (MPRAGE) improves the classification of cortical lesions by allowing more accurate anatomic localization of lesion morphology. Methods 11 patients with MS with previously identified cortical lesions were scanned using DIR, PSIR, and 3D MPRAGE. Lesions were identified on DIR and PSIR and classified as purely intracortical or mixed. MPRAGE images were then examined, and lesions were re-classified based on the new information. Results The high signal-to-noise ratio, fine anatomic detail, and clear gray-white matter tissue contrast seen in the MPRAGE images provided superior delineation of lesion borders and surrounding gray-white matter junction, improving classification accuracy. 119 lesions were identified as either intracortical or mixed on DIR/PSIR. In 89 cases, MPRAGE confirmed the classification by DIR/PSIR. In 30 cases, MPRAGE overturned the original classification. Conclusion Improved classification of cortical lesions was realized by inclusion of high-spatial resolution 3D MPRAGE. This sequence provides unique detail on lesion morphology that is necessary for accurate classification.

Diagnosis for Engine Misfire Fault Based on Torsional Vibration and Neural-Network Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 7240-7246
Author(s):  
Can Yi Du ◽  
Kang Ding ◽  
Zhi Jian Yang ◽  
Cui Li Yang
Keyword(s):  
Neural Network ◽  
Torsional Vibration ◽  
Bp Neural Network ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Correction Method ◽  
Operating Conditions ◽  
Diagnostic Model ◽  
Vibration Signal Analysis

Misfire is a common fault which affects the engine performances. Because the signal-to-noise ratio of torsional vibration signal is high, torsional vibration test and analysis for the engine were performed in a variety of operating conditions, including healthy condition and single-cylinder misfire condition. In order to improve the accuracy of analysis, energy centrobaric correction method was used to correct the amplitude. Taking the corrected amplitude of main order as the fault feature, and then a BP neural-network diagnostic model can be established for misfire diagnosis. The result shows that the method of combining torsional vibration signal analysis and neural-network can diagnose engine misfire fault correctly.

scholarly journals Information Hiding using LSB Technique based on Developed PSO Algorithm

2018 ◽  
Vol 8 (2) ◽  
pp. 1156 ◽  
Author(s):  
Wisam Abed Shukur ◽  
Khalid Kadhim Jabbar
Keyword(s):  
Information Hiding ◽  
Particle Swarm Optimization Algorithm ◽  
Signal To Noise Ratio ◽  
Pso Algorithm ◽  
Search Space ◽  
Secret Message ◽  
Stego Image ◽  
Carrier Medium ◽  
Lsb Technique ◽  
Lsb Substitution

<p>Generally, The sending process of secret information via the transmission channel or any carrier medium is not secured. For this reason, the techniques of information hiding are needed. Therefore, steganography must take place before transmission. To embed a secret message at optimal positions of the cover image under spatial domain, using the developed particle swarm optimization algorithm (Dev.-PSO) to do that purpose in this paper based on Least Significant Bits (LSB) using LSB substitution. The main aim of (Dev. -PSO) algorithm is determining an optimal paths to reach a required goals in the specified search space based on disposal of them, using (Dev.-PSO) algorithm produces the paths of a required goals with most efficient and speed. An agents population is used in determining process of a required goals at search space for solving of problem. The (Dev.-PSO) algorithm is applied to different images; the number of an image which used in the experiments in this paper is three. For all used images, the Peak Signal to Noise Ratio (PSNR) value is computed. Finally, the PSNR value of the stego-A that obtained from blue sub-band colo is equal (44.87) dB, while the stego-B is equal (44.45) dB, and the PSNR value for the stego-C is (43.97)dB, while the vlue of MSE that obtained from the same color sub-bans is (0.00989), stego-B equal to (0.01869), and stego-C is (0.02041). Furthermore, our proposed method has ability to survive the quality for the stego image befor and after hiding stage or under intended attack that used in the existing paper such as Gaussian noise, and salt &amp; pepper noise.</p>

scholarly journals 19F MRI Imaging Strategies to Reduce Isoflurane Artifacts in In Vivo Images

2021 ◽  
Author(s):  
Alexander H. J. Staal ◽  
Andor Veltien ◽  
Mangala Srinivas ◽  
Tom W. J. Scheenen
Keyword(s):  
Signal To Noise Ratio ◽  
Image Interpretation ◽  
Region Of Interest ◽  
Signal Acquisition ◽  
Mri Imaging ◽  
Rapid Acquisition ◽  
Selective Suppression ◽  
3D Sequence ◽  
Frequency Selective

Abstract Purpose Isoflurane (ISO) is the most commonly used preclinical inhalation anesthetic. This is a problem in 19F MRI of fluorine contrast agents, as ISO signals cause artifacts that interfere with unambiguous image interpretation and quantification; the two most attractive properties of heteronuclear MRI. We aimed to avoid these artifacts using MRI strategies that can be applied by any pre-clinical researcher. Procedures Three strategies to avoid ISO chemical shift displacement artifacts (CSDA) in 19F MRI are described and demonstrated with measurements of 19F-containing agents in phantoms and in vivo (n = 3 for all strategies). The success of these strategies is compared to a standard Rapid Acquisition with Relaxation Enhancement (RARE) sequence, with phantom and in vivo validation. ISO artifacts can successfully be avoided by (1) shifting them outside the region of interest using a narrow signal acquisition bandwidth, (2) suppression of ISO by planning a frequency-selective suppression pulse before signal acquisition or by (3) preventing ISO excitation with a 3D sequence with a narrow excitation bandwidth. Results All three strategies result in complete ISO signal avoidance (p < 0.0001 for all methods). Using a narrow acquisition bandwidth can result in loss of signal to noise ratio and distortion of the image, and a frequency-selective suppression pulse can be incomplete when B1-inhomogeneities are present. Preventing ISO excitation with a narrow excitation pulse in a 3D sequence yields the most robust results (relative SNR 151 ± 28% compared to 2D multislice methods, p = 0.006). Conclusion We optimized three easily implementable methods to avoid ISO signal artifacts and validated their performance in phantoms and in vivo. We make recommendation on the parameters that pre-clinical studies should report in their method section to make the used approach insightful.

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