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- 实验方案
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Contribution of Minimum Noise Fraction Transformation of Multi-temporal RADARSAT-2 Polarimetric SAR Data to Cropland Classification
摘要： Agriculture is an important sector in Canada, and annual crop inventories are required in many agricultural applications. Multi-temporal polarimetric synthetic aperture radar (SAR) data have great potential in crop classification due to its less dependency on weather condition. This study, for the first time, investigated the effects of the Minimum Noise Fraction (MNF) transformation of multi-temporal RADARSAT-2 polarimetric SAR data on the performance of cropland classification through the discussing of the performance of different polarimetric SAR parameter sets, and the impact of the timing of RADARSAT-2 datasets in southwestern Ontario. The random forest classifier was adopted due to its excellent ability in crop classification. The results illustrated that the elements of coherency matrix performed the best in agricultural land cover classification. The multi-temporal polarimetric SAR data acquired from the end of June to November gave the best classification accuracy, and an overall accuracy of 90% can be achieved using two images acquired in the middle of September and October. The MNF transformation can further improve the classification accuracy, and this accuracy was competitive with the accuracy produced using the integration of optical and polarimetric SAR data.
关键词： Minimum Noise Fraction,RADARSAT-2,random forest classifier,polarimetric SAR,cropland classification
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE High Power Diode Lasers and Systems Conference (HPD) - Coventry, United Kingdom (2019.10.9-2019.10.10)] 2019 IEEE High Power Diode Lasers and Systems Conference (HPD) - The requirements on pulsed laser diodes for use in atmospheric LiDAR
摘要： A novel technique for parameterizing surface roughness in coastal inundation models using airborne laser scanning (lidar) data is presented. Two important parameters to coastal overland flow dynamics, Manning’s n (bottom friction) and effective aerodynamic roughness length (wind speed reduction), are computed based on a random forest (RM) regression model trained using field measurements from 24 sites in Florida fused with georegistered lidar point cloud data. The lidar point cloud for each test site is separated into ground and nonground classes and the z-dimensional (height or elevation) variance from the least squares regression plane is computed, along with the height of the nonground regression plane. These statistics serve as the predictor variables in the parameterization model. The model is then tested using a bootstrap subsampling procedure consisting of removal without replacement of one record and using the surviving records to train the model and predict the surface roughness parameter of the removed record. When compared with the industry standard technique of assigning surface roughness parameters based on published land use/land cover type, the RM regression models reduce the parameterization error by 93% (0.086–0.006) and 53% (1.299–0.610 m) for Manning’s n and effective aerodynamic roughness length, respectively. These improvements will improve water level and velocity predictions in coastal models.
关键词： lidar,Manning’s n,random forest (RM),land cover,Aerodynamic roughness
更新于2025-09-23 15:19:57
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Automatic hip geometric feature extraction in dxa imaging using regional random forest
摘要： BACKGROUND: Hip fracture is considered one of the salient disability factors across the global population. People with hip fractures are prone to become permanently disabled or die from complications. Although currently the premier determiner, bone mineral density has some notable limitations in terms of hip fracture risk assessment. OBJECTIVES: To learn more about bone strength, hip geometric features (HGFs) can be collected. However, organizing a hip fracture risk study for a large population using a manual HGFs collection technique would be too arduous to be practical. Thus, an automatic HGFs extraction technique is needed. METHOD: This paper presents an automated HGFs extraction technique using regional random forest. Regional random forest localizes landmark points from femur DXA images using local constraints of hip anatomy. The local region constraints make random forest robust to noise and increase its performance because it processes the least number of points and patches. RESULTS: The proposed system achieved an overall accuracy of 96.22% and 95.87% on phantom data and real human scanned data respectively. CONCLUSION: The proposed technique’s ability to measure HGFs could be useful in research on the cause and facts of hip fracture and could help in the development of new guidelines for hip fracture risk assessment in the future. The technique will reduce workload and improve the use of X-ray devices.
关键词： contour traversing,random forest,DXA imaging system,hip geometric features
更新于2025-09-19 17:15:36
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Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay
摘要： Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate.
关键词： water quality,total suspended solids,ocean color,satellite remote sensing,statistical analysis,Random Forest,Chesapeake Bay,multispectral,machine learning
更新于2025-09-19 17:15:36
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Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification
摘要： Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and random forest classification (RFC) for a section of an asphalt road that is located in the suburb of Shihezi City in Xinjiang Province of China. After a spectral and spatial feature analysis of pavement distress, a total of 48 multidimensional and multiscale features were extracted based on the strength of the point cloud elevations and reflection intensities. Subsequently, we extracted the pavement distresses from the multifeature dataset by utilizing the RFC method. The overall accuracy of the distress identification was 92.3%, and the kappa coefficient was 0.902. When compared with the maximum likelihood classification (MLC) and support vector machine (SVM), the RFC had a higher accuracy, which confirms its robustness and applicability to multisample and high-dimensional data classification. Furthermore, the method achieved an overall accuracy of 95.86% with a validation dataset. This result indicates the validity and stability of our method, which highway maintenance agencies can use to evaluate road health conditions and implement maintenance.
关键词： UAV,random forest classification,pavement health conditions,LiDAR,asphalt pavement distresses,multiscale features
更新于2025-09-19 17:15:36
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Riparian trees genera identification based on leaf-on/leaf-off airborne laser scanner data and machine learning classifiers in northern France
摘要： Riparian forests are valuable environments delivering multiples ecological services. Because they face both natural and anthropogenic constraints, riparian forests need to be accurately mapped in terms of genera/species diversity. Previous studies have shown that the Airborne Laser Scanner (ALS) data have the potential to classify trees in di?erent contexts. However, an assessment of important features and classi?cation results for broadleaved deciduous riparian forests mapping using ALS remains to be achieved. The objective of this study was to estimate which features derived from ALS data were important for describing trees genera from a riparian deciduous forest, and provide results of classi?cations using two Machine Learning algorithms. The procedure was applied to 191 trees distributed in eight genera located along the Sélune river in Normandy, northern France. ALS data from two surveys, in the summer and winter, were used. From these data, trees crowns were extracted and global morphology and internal structure features were computed from the 3D points clouds. Five datasets were established, containing for each one an increasing number of genera. This was implemented in order to assess the level of discrimination between trees genera. The most discriminant features were selected using a stepwise Quadratic Discriminant Analysis (sQDA) and Random Forest, allowing the number of features to be reduced from 144 to 3–9, depending on the datasets. The sQDA-selected features highlighted the fact that, with an increasing number of genera in the datasets, internal structure became more discrimi- nant. The selected features were used as variables for classi?cation using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Additionally, Random Forest classi?cations were conducted using all features computed, without selection. The best classi?ca- tion performances showed that using the sQDA-selected features with SVM produced accuracy ranging from 83.15% when using three genera (Oak, Alder and Poplar). A similar result was obtained using RF and all features available for classi?cation. The latter also achieved the best classi?cation performances when using seven and eight genera. The results highlight that ML algorithms are suitable methods to map riparian trees.
关键词： Machine Learning,Riparian forests,tree genera identification,Support Vector Machine (SVM),Airborne Laser Scanner (ALS),Random Forest (RF)
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Dust Abrasion Damage on Martian Solar Arrays: Experimental Investigation and Opportunity Rover Performance Analysis
摘要： A novel technique for parameterizing surface roughness in coastal inundation models using airborne laser scanning (lidar) data is presented. Two important parameters to coastal overland flow dynamics, Manning’s n (bottom friction) and effective aerodynamic roughness length (wind speed reduction), are computed based on a random forest (RM) regression model trained using field measurements from 24 sites in Florida fused with georegistered lidar point cloud data. The lidar point cloud for each test site is separated into ground and nonground classes and the z-dimensional (height or elevation) variance from the least squares regression plane is computed, along with the height of the nonground regression plane. These statistics serve as the predictor variables in the parameterization model. The model is then tested using a bootstrap subsampling procedure consisting of removal without replacement of one record and using the surviving records to train the model and predict the surface roughness parameter of the removed record. When compared with the industry standard technique of assigning surface roughness parameters based on published land use/land cover type, the RM regression models reduce the parameterization error by 93% (0.086–0.006) and 53% (1.299–0.610 m) for Manning’s n and effective aerodynamic roughness length, respectively. These improvements will improve water level and velocity predictions in coastal models.
关键词： lidar,Manning’s n,random forest (RM),land cover,Aerodynamic roughness
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE Industry Applications Society Annual Meeting - Baltimore, MD, USA (2019.9.29-2019.10.3)] 2019 IEEE Industry Applications Society Annual Meeting - Assessing the Modelling Approach and Datasets Required for Fault Detection in Photovoltaic Systems
摘要： Reliable monitoring for photovoltaic assets (PVs) is essential to ensuring uptake, long term performance, and maximum return on investment of renewable systems. To this end this paper investigates the input data and machine learning techniques required for day-behind predictions of PV generation, within the scope of conducting informed maintenance of these systems. Five years of PV generation data at hourly intervals were retrieved from four commercial building-mounted PV installations in the UK, as well as weather data retrieved from MIDAS. A support vector machine, random forest and artificial neural network were trained to predict PV power generation. Random forest performed best, achieving an average mean relative error of 2.7%. Irradiance, previous generation and solar position were found to be the most important variables. Overall, this work shows how low-cost data driven analysis of PV systems can be used to support the effective management of such assets.
关键词： weather data,random forest,machine learning,photovoltaics,Fault detection
更新于2025-09-16 10:30:52
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[IEEE 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) - Chengdu, China (2019.5.25-2019.5.28)] 2019 2nd International Conference on Artificial Intelligence and Big Data (ICAIBD) - ABC-SVM and PSO-RF Model for Photovoltaic Forecasting Based on Big Data
摘要： Prediction of photovoltaic output is of great significance to the stable operation of microgrid system. Firstly, the artificial bee colony based support mechine (ABC-SVM) method is used to train historical meteorological data and photovoltaic output data, which can divide the weather condition into four categories. Secondly, tens of thousands of data are selected under four types of meteorological conditions, and each group of data is trained by particle swarm optimization based random forest (PSO-RF) model. After training, the four different PSO-RF model with different parameters can be obtained for the photovoltaic forecasting individually. Finally, we collect weather information and photovoltaic data from a microgrid station in Yangjiang Guangdong province to test our combined model. Numerical results show that the proposed approach achieves better prediction accuracy than the simple SVR and traditional RF methods.
关键词： random forest,support vector machine,particle swarm optimization,artificial bee colony
更新于2025-09-16 10:30:52
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Machine learning based temperature prediction of poly( <i>N</i> -isopropylacrylamide)-capped plasmonic nanoparticle solutions
摘要： The temperature-dependent optical properties of gold nanoparticles that are capped with the thermo-sensitive polymer: ‘poly(N-isopropylacrylamide)’ (PNIPAM), have been studied extensively for several years. Also, their suitability to function as nanoscopic thermometers for bio-sensing applications has been suggested numerous times. In an attempt to establish this, many have studied the temperature-dependent optical resonance characteristics of these particles; however, developing a simple mathematical relationship between the optical measurements and the solution temperature remains an open challenge. In this paper, we attempt to systematically address this problem using machine learning techniques to quickly and accurately predict the solution-temperature, based on spectroscopic data. Our emphasis is on establishing a simple and practically useful solution to this problem. Our dataset comprises spectroscopic absorption data from both nanorods and nanobipyramids capped with PNIPAM, measured at discretely varied and pre-set temperature states. Specific regions of the spectroscopic data are selected as features for prediction using random forest (RF), gradient boosting (GB) and adaptive boosting (AB) regression techniques. Our prediction results indicate that RF and GB techniques can be used successfully to predict solution temperatures instantly to within 1 1C of accuracy.
关键词： PNIPAM,spectroscopic data,temperature prediction,adaptive boosting,machine learning,random forest,gradient boosting,gold nanoparticles
更新于2025-09-16 10:30:52