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Journal articles on the topic 'RF mapping'
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1
Schmitz, B., J. Köszegi, K. Alomari, O. Kugeler, and J. Knobloch. "Magnetometric mapping of superconducting RF cavities." Review of Scientific Instruments 89, no. 5 (May 2018): 054706. http://dx.doi.org/10.1063/1.5030509.
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Bonagura, V. R., S. E. Artandi, A. Davidson, I. Randen, N. Agostino, K. Thompson, J. B. Natvig, and S. L. Morrison. "Mapping studies reveal unique epitopes on IgG recognized by rheumatoid arthritis-derived monoclonal rheumatoid factors." Journal of Immunology 151, no. 7 (October 1, 1993): 3840–52. http://dx.doi.org/10.4049/jimmunol.151.7.3840.
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Abstract We have used chimeric IgG antibodies and their genetically engineered variants prepared by a combination of site-directed mutagenesis and exon exchange to define the structure(s) on IgG recognized by monoclonal rheumatoid factor (RF) autoantibodies from rheumatoid arthritis (RA) patients. Nineteen RF produced by EBV-transformed cell lines from the synovium or blood of RA patients were analyzed. Their binding patterns differ significantly from those seen with RF obtained from patients with Waldenstrom's macroglobulinemia (WMac). Half of the RA-derived RF bound IgG1, 2, and 4, but not 3 (Ga specificity), the common pattern in WMac. However, heterogeneity in fine specificity within the Ga reactivity pattern was observed. Moreover, seven others bound all four IgG subclasses, a pattern observed for only one WMac-derived RF from a patient who also had RA. Three RF had subclass specificities unlike any observed with WMac-derived RF. Most RA-derived RF bound IgG at a discontinuous epitope comprised of residues from both the CH2 and CH3 H chain constant regions. However, unlike any WMac-derived RF, one RA-derived RF bound IgG in CH2, another in CH3, and a third at an undetermined site outside of the CH2-CH3 interface. Some RA-derived RF bound aglycosylated IgG4 less well than glycosylated IgG4, suggesting that the carbohydrate moiety was important in establishing their binding epitope in CH2. These studies demonstrate that the repertoire of RF expressed by RA patients contains some unique binding specificities for IgG epitopes not found among our panel of WMac-derived RF. Our results therefore call into question whether WMac-derived RF with their limited diversity are appropriate models for disease-related RF. In addition, RF with their multiple specificities can serve as probes of antibody structure.
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Ouma, Y., B. Nkwae, D. Moalafhi, P. Odirile, B. Parida, G. Anderson, and J. Qi. "COMPARISON OF MACHINE LEARNING CLASSIFIERS FOR MULTITEMPORAL AND MULTISENSOR MAPPING OF URBAN LULC FEATURES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 681–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-681-2022.
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Abstract. This study compares four machine-learning algorithms comprising of Classification And Regression Trees (CART), Random Forest (RF), Gradient Tree Boosting (GTB) and Support Vector Machine (SVM) for the classification of urban land-use and land-cover (LULC) features. Using multitemporal and multisensor Landsat data from 1984-2020 at 5-year intervals for the Greater Gaborone Planning Area (GGPA) in Botswana, the aim of the study is to determine the performance of the classifiers in the extraction of different urban LULC features as built-up, bare-soil, water, grass, shrubs and forest. The results show that for mapping built-up areas, RF and SVM presented the best results with overall accuracy of 85%. Bare soil is best mapped using RF and CART with accuracy of up to 98%, while SVM and GTB were most suitable for mapping water bodies. The suitable classifiers for mapping the vegetation classes were RF for grass (94.5%), SVM for shrubland (81.5%) and GTB for forest (84.3%). In terms of class specific accuracy, RF achieved the highest performance with average overall accuracy (OA) of 95.9%, SVM (95.8%), GTB (95.6%) and CART (95.1%). The same performance pattern was observed from the F1-score, True Positive Rate (TPR), False Positive Rate (FPR) and Area under ROC curve (AUC) metrices for the class classification accuracies. The overall accuracy for the eight-epoch years were RF (87.8%), SVM (87.5%), GTB (86.4%) and CART (85.3%). To improve on the urban LULC mapping, the study proposes the post-classification feature fusion of the best classifier results.
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Akbari, Elahe, Ali Darvishi Boloorani, Najmeh Neysani Samany, Saeid Hamzeh, Saeid Soufizadeh, and Stefano Pignatti. "Crop Mapping Using Random Forest and Particle Swarm Optimization based on Multi-Temporal Sentinel-2." Remote Sensing 12, no. 9 (May 3, 2020): 1449. http://dx.doi.org/10.3390/rs12091449.
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Timely and accurate information on crop mapping and monitoring is necessary for agricultural resources management. Accordingly, the applicability of the proposed classification-feature selection ensemble procedure with different feature sets for crop mapping is investigated. Here, we produced various feature sets including spectral bands, spectral indices, variation of spectral index, texture, and combinations of features to map different types of crops. By using various feature sets and the random forest (RF) classifier, the crop maps were created. In aiming to determine the most relevant and distinctive features, the particle swarm optimization (PSO) and RF-variable importance measure feature selection methods were examined. The classification-feature selection ensemble procedure was adapted to combine the outputs of different feature sets from the better feature selection method using majority votes. Multi-temporal Sentinel-2 data has been used in Ghale-Nou county of Tehran, Iran. The performance of RF was efficient in crop mapping especially by spectral bands and texture in combination with other feature sets. Our results showed that the PSO-based feature selection leads to a more accurate classification than the RF-variable importance measure, in almost all feature sets for all crop types. The RF classifier-PSO ensemble procedure for crop mapping outperformed the RF classifier in each feature set with regard to the class-wise and overall accuracies (OA) (of about 2.7–7.4% increases in OA and 0.48–3.68% (silage maize), 0–1.61% (rice), 2.82–15.43% (alfalfa), and 10.96–41.13% (vegetables) improvement in F-scores for all feature sets). The proposed method could mainly be useful to differentiate between heterogeneous crop fields (e.g., vegetables in this study) due to their more obtained omission/commission errors reduction.
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Abida, Khouloud, Meriem Barbouchi, Khaoula Boudabbous, Wael Toukabri, Karem Saad, Habib Bousnina, and Thouraya Sahli Chahed. "Sentinel-2 Data for Land Use Mapping: Comparing Different Supervised Classifications in Semi-Arid Areas." Agriculture 12, no. 9 (September 9, 2022): 1429. http://dx.doi.org/10.3390/agriculture12091429.
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Mapping and monitoring land use (LU) changes is one of the most effective ways to understand and manage land transformation. The main objectives of this study were to classify LU using supervised classification methods and to assess the effectiveness of various machine learning methods. The current investigation was conducted in the Nord-Est area of Tunisia, and an optical satellite image covering the study area was acquired from Sentinel-2. For LU mapping, we tested three machine learning models algorithms: Random Forest (RF), K-Dimensional Trees K-Nearest Neighbors (KDTree-KNN) and Minimum Distance Classification (MDC). According to our research, the RF classification provided a better result than other classification models. RF classification exhibited the best values of overall accuracy, kappa, recall, precision and RMSE, with 99.54%, 0.98%, 0.98%, 0.98% and 0.23%, respectively. However, low precision was observed for the MDC method (RMSE = 1.15). The results were more intriguing since they highlighted the value of the bare soil index as a covariate for LU mapping. Our results suggest that Sentinel-2 combined with RF classification is efficient for creating a LU map.
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Merfeldas, Audrius, Pranas Kuzas, Darius Gailius, Zilvinas Nakutis, Mindaugas Knyva, Algimantas Valinevicius, Darius Andriukaitis, Mindaugas Zilys, and Dangirutis Navikas. "An Improved Near-field Magnetic Probe Radiation Profile Boundaries Assessment for Optimal Radiated Susceptibility Pre-Mapping." Symmetry 12, no. 7 (June 28, 2020): 1063. http://dx.doi.org/10.3390/sym12071063.
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In this paper, the near-field radiated susceptibility pre-mapping method is proposed using the improved near-field probe power radiation profile assessment. The modelling of the electromagnetic field strength in 80–3000 MHz range in the proximity of the near-field probe was performed. The −6 dB aperture boundaries of the near-field probe and their variation, due to the proximity of the radio frequency (RF) printed circuit board (PCB) components, were determined, while the aperture map distortion, arising from the proximity of the passive RF PCB components were evaluated. The scanning path requirements for the RF susceptibility mapping were determined. The simulation of improved near-field probe absolute magnetic field strength reference map in open-air conditions is carried out in this work. The comparative analysis using the absolute maximum difference metric of orthogonal absolute magnetic field map cross-sections between the reference map and magnetic field maps affected by the proximity of the components was carried out. The experimental study of the RF amplifier stage susceptibility map with susceptibility mapping measurement results are presented in this work.
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Pasquali, J. L., A. M. Knapp, A. Farradji, and A. Weryha. "Mapping of four light chain-associated idiotopes of a human monoclonal rheumatoid factor." Journal of Immunology 139, no. 3 (August 1, 1987): 818–23. http://dx.doi.org/10.4049/jimmunol.139.3.818.
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Abstract In an effort to analyze both IgM rheumatoid factor (RF) repertoire and regulation of RF production in humans, we developed a panel of four mouse monoclonal antibodies (mAb) defining distinct K light chain-associated idiotopes (id) of a human monoclonal IgM RF (Alt). These mAb (A75, AM1, AM2, AM3) had equivalent reactivities with the immunizing RF during classic inhibition of antigen-binding assays. These anti-id reagents were reacting to neither other tested monoclonal IgM RF nor normal polyclonal IgM. It was possible to distinguish the id defined by the mAb from the results of four sets of experiments: dissociation of Alt RF heavy (H) and light (L) chains showed that A75, AM1, and AM2 reacted to id located on the L chain, whereas AM3 defined a conformational RF id; recombination experiments of H and L chains showed that A75 and AM2 reacted well with both homologous (Alt H + Alt L) and heterologous (Alt L + unrelated H) recombinants, whereas AM1 reacted better with the homologous recombinant than with the heterologous one; the relative affinities of the mAb were drawn from their ability to shift already bound labeled Alt RF from solid phase IgG; and radiolabeling of two mAb (A75 and AM3) and experiments of inhibition of id binding with cold anti-id and cold anti-CK showed that A75 recognized a proximal id (close to the K constant region), whereas AM3 defined a more distal id, AM2 and AM1 being located between A75- and AM3-defined sites. This topographic mapping of K light chain-associated id of a human RF with anti-id of known relative affinities could help in studying idiotypic regulation in humans.
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Wicaksono, Pramaditya, Prama Ardha Aryaguna, and Wahyu Lazuardi. "Benthic Habitat Mapping Model and Cross Validation Using Machine-Learning Classification Algorithms." Remote Sensing 11, no. 11 (May 29, 2019): 1279. http://dx.doi.org/10.3390/rs11111279.
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This research was aimed at developing the mapping model of benthic habitat mapping using machine-learning classification algorithms and tested the applicability of the model in different areas. We integrated in situ benthic habitat data and image processing of WorldView-2 (WV2) image to parameterise the machine-learning algorithm, namely: Random Forest (RF), Classification Tree Analysis (CTA), and Support Vector Machine (SVM). The classification inputs are sunglint-free bands, water column corrected bands, Principle Component (PC) bands, bathymetry, and the slope of underwater topography. Kemujan Island was used in developing the model, while Karimunjawa, Menjangan Besar, and Menjangan Kecil Islands served as test areas. The results obtained indicated that RF was more accurate than any other classification algorithm based on the statistics and benthic habitats spatial distribution. The maximum accuracy of RF was 94.17% (4 classes) and 88.54% (14 classes). The accuracies from RF, CTA, and SVM were consistent across different input bands for each classification scheme. The application of RF model in the classification of benthic habitat in other areas revealed that it is recommended to make use of the more general classification scheme in order to avoid several issues regarding benthic habitat variations. The result also established the possibility of mapping a benthic habitat without the use of training areas.
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Kwak, Geun-Ho, Chan-won Park, Kyung-do Lee, Sang-il Na, Ho-yong Ahn, and No-Wook Park. "Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data." Remote Sensing 13, no. 9 (April 21, 2021): 1629. http://dx.doi.org/10.3390/rs13091629.
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When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with similar spectral characteristics, leading to poor classification accuracy. In particular, limited input data are the main obstacles to obtain reliable classification results for early crop mapping. This study investigates the potential of a hybrid classification approach, i.e., CNN-random forest (CNN-RF), in the context of early crop mapping, that combines the automatic feature extraction capability of CNN with the superior discrimination capability of an RF classifier. Two experiments on incremental crop classification with unmanned aerial vehicle images were conducted to compare the performance of CNN-RF with that of CNN and RF with respect to the length of the time-series and training data sizes. When sufficient time-series images and training data were used for the classification, the accuracy of CNN-RF was slightly higher or comparable with that of CNN. In contrast, when fewer images and the smallest training data were used at the early crop growth stage, CNN-RF was substantially beneficial and the overall accuracy increased by maximum 6.7%p and 4.6%p in the two study areas, respectively, compared to CNN. This is attributed to its ability to discriminate crops from features with insufficient information using a more sophisticated classifier. The experimental results demonstrate that CNN-RF is an effective classifier for early crop mapping when only limited input images and training samples are available.
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Khalighi, Mohammad Mehdi, Brian K. Rutt, and Adam B. Kerr. "Adiabatic RF pulse design for Bloch-SiegertB+ mapping." Magnetic Resonance in Medicine 70, no. 3 (October 5, 2012): 829–35. http://dx.doi.org/10.1002/mrm.24507.
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Chen, Wei, Zhao Wang, Guirong Wang, Zixin Ning, Boxiang Lian, Shangjie Li, Paraskevas Tsangaratos, Ioanna Ilia, and Weifeng Xue. "Optimizing Rotation Forest-Based Decision Tree Algorithms for Groundwater Potential Mapping." Water 15, no. 12 (June 19, 2023): 2287. http://dx.doi.org/10.3390/w15122287.
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Groundwater potential mapping is an important prerequisite for evaluating the exploitation, utilization, and recharge of groundwater. The study uses BFT (best-first decision tree classifier), CART (classification and regression tree), FT (functional trees), EBF (evidential belief function) benchmark models, and RF-BFTree, RF-CART, and RF-FT ensemble models to map the groundwater potential of Wuqi County, China. Firstly, select sixteen groundwater spring-related variables, such as altitude, plan curvature, profile curvature, curvature, slope angle, slope aspect, stream power index, topographic wetness index, stream sediment transport index, normalized difference vegetation index, land use, soil, lithology, distance to roads, distance to rivers, and rainfall, and make a correlation analysis of these sixteen groundwater spring-related variables. Secondly, optimize the parameters of the seven models and select the optimal parameters for groundwater modeling in Wuqi County. The predictive performance of each model was evaluated by estimating the area under the receiver operating characteristic (ROC) curve (AUC) and statistical index (accuracy, sensitivity, and specificity). The results show that the seven models have good predictive capabilities, and the ensemble model has a larger AUC value. Among them, the RF-BFT model has the highest success rate (AUC = 0.911), followed by RF-FT (0.898), RF-CART (0.894), FT (0.852), EBF (0.824), CART (0.801), and BFtree (0.784), respectively. Groundwater potential maps of these 7 models were obtained, and four different classification methods (geometric interval, natural breaks, quantile, and equal interval) were used to reclassify the obtained GPM into 5 categories: very low (VLC), low (LC), moderate (MC), high (HC), and very high (VHC). The results show that the natural breaks method has the best classification performance, and the RF-BFT model is the most reliable. The study highlights that the proposed ensemble model has more efficient and accurate performance for groundwater potential mapping.
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Pipa, Gordon, Zhe Chen, Sergio Neuenschwander, Bruss Lima, and Emery N. Brown. "Mapping of Visual Receptive Fields by Tomographic Reconstruction." Neural Computation 24, no. 10 (October 2012): 2543–78. http://dx.doi.org/10.1162/neco_a_00334.
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The moving bar experiment is a classic paradigm for characterizing the receptive field (RF) properties of neurons in primary visual cortex (V1). Current approaches for analyzing neural spiking activity recorded from these experiments do not take into account the point-process nature of these data and the circular geometry of the stimulus presentation. We present a novel analysis approach to mapping V1 receptive fields that combines point-process generalized linear models (PPGLM) with tomographic reconstruction computed by filtered-back projection. We use the method to map the RF sizes and orientations of 251 V1 neurons recorded from two macaque monkeys during a moving bar experiment. Our cross-validated goodness-of-fit analyses show that the PPGLM provides a more accurate characterization of spike train data than analyses based on rate functions computed by the methods of spike-triggered averages or first-order Wiener-Volterra kernel. Our analysis leads to a new definition of RF size as the spatial area over which the spiking activity is significantly greater than baseline activity. Our approach yields larger RF sizes and sharper orientation tuning estimates. The tomographic reconstruction paradigm further suggests an efficient approach to choosing the number of directions and the number of trials per direction in designing moving bar experiments. Our results demonstrate that standard tomographic principles for image reconstruction can be adapted to characterize V1 RFs and that two fundamental properties, size and orientation, may be substantially different from what is currently reported.
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Sriprom, Chatyapa, Supaluck Kanjanauthai, and Anon Jantanukul. "การสร้างภาพสามมิติ (3D Mapping) ในกลุ่มผู้ป่วยภาวะหัวใจเต้นผิดจังหวะชนิดเร็วที่ได้รับการรักษาโดยการจี้ด้วยกระแสไฟฟ้า (Ablation)." Siriraj Medical Bulletin 14, no. 3 (July 1, 2021): 53–60. http://dx.doi.org/10.33192/smb.v14i3.249578.
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ในปัจจุบันการสร้างภาพสามมิติ (3D Mapping) ในกลุ่มผู้ป่วยภาวะหัวใจเต้นผิดจังหวะชนิดเร็วที่ได้รับการรักษาโดยการจี้ด้วยกระแสไฟฟ้า (RF Ablation) ได้รับความนิยมอย่างแพร่หลายมากขึ้น เนื่องจากการสร้างภาพสามมิติ มีการนำเทคโนโลยีสมัยใหม่เข้ามาช่วยในการสร้างภาพได้แก่ Magnetic Technology, Current-based technology, Hybrid technology ทำให้สามารถสร้างภาพสามมิติออกมาได้หลายรูปแบบอย่างเช่น Anatomical mapping, Local Activation Time mapping (LAT), Bipolar Voltage mapping, Complex Fractionated Atrial Electrogram (CFAEs) Map, Pace map, Merge หรือ Fusion, Reentrant map เป็นต้นโดยภาพที่ได้นอกจากจะแสดงเป็นภาพนิ่งแล้วยังสามารถแสดงเป็น Video Animation ได้อีกด้วยอย่างเช่น Propagation Map, Ripple Map เป็นต้น ทำให้มีความแม่นยำในการรักษา ผู้ป่วยได้รับปริมาณรังสีที่น้อยลง มีความปลอดภัย และลดภาวะแทรกซ้อน ซึ่งก่อให้เกิดประโยชน์สูงสุดแก่ผู้ป่วย
คำสำคัญ: การสร้างภาพสามมิติ, การจี้ด้วยกระแสไฟฟ้า
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Zhao, Zhi-Dong, Ming-Song Zhao, Hong-Liang Lu, Shi-Hang Wang, and Yuan-Yuan Lu. "Digital Mapping of Soil pH Based on Machine Learning Combined with Feature Selection Methods in East China." Sustainability 15, no. 17 (August 25, 2023): 12874. http://dx.doi.org/10.3390/su151712874.
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This study aimed to evaluate and compare the performances of the random forest (RF) and support vector regression (SVR) models combined with different feature selection methods, including recursive feature elimination (RFE), simulated annealing feature selection (SAFS), and selection by filtering (SBF) in predicting soil pH in Anhui Province, East China. We also used the ALL original features to build the RF and SVR models as a comparison. A total of 140 samples were selected, following the principles of randomness, uniformity, and representativeness, to consider the combination of landscape elements, such as topography, parent material, and land use. Auxiliary data, including climatic, topographic, and vegetation indexes, were used for predicting soil pH. The results showed that compared with the use the ALL original modeling features (ALL-RF, ALL-SVR), the combination of the three feature selection algorithms with RF and SVR can eliminate some redundant features and effectively improve the prediction accuracy of the soil pH model. For the RF model, the RMSE and the MAE of the calibration of the RFE-RF model were 0.73 and 0.57 and had the highest R2 in four different RF models. The testing set of the RFE-RF model had an R2 of 0.61, which was better than that of the ALL-RF (R2 = 0.45) model and lower than those of the SAFS-RF (R2 = 0.71) and SBF-RF (R2 = 0.69) models. For the SVR model, the RFE-RF model was more robust and had better generalization ability. The accuracy of digital soil mapping can be improved through feature selection.
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Eghrari, Z., M. R. Delavar, M. Zare, A. Beitollahi, and B. Nazari. "LAND SUBSIDENCE SUSCEPTIBILITY MAPPING USING MACHINE LEARNING ALGORITHMS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 129–36. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-129-2023.
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Abstract. Land subsidence (LS) is one of the most challenging natural disasters that has potential consequences such as damage to infrastructures and buildings, creating sinkholes, and leading to soil destruction. To mitigate the damages caused by LS, it is necessary to determine the LS-prone areas. In this paper, LS susceptibility was assessed for Kashan Plain in Iran using Random Forest (RF) and XGBoost machine learning algorithms. For the susceptibility analysis, twelve influential factors including elevation, slope, aspect, curvature, topographic wetness index (TWI), groundwater drawdown (GWD), normalized difference vegetation index (NDVI), distance to stream (DtS), distance to road (DtR), distance to fault (DtF), lithology, and land use were taken into account. 291 LS points were used in this study which was divided into two parts of 70% and 30% for training and testing the models, respectively. The prediction power of the models and their produced LS susceptibility maps (LSSMs) were validated using the Root Mean Square Error (RMSE), R-Squared (R2), and Mean Absolute Error (MAE) values. The results showed that the XGBoost had a higher R² equal to 0.9032 compared to that of the RF which was equal to 0.8355. XGBoost model had an RMSE equal to 0.3764 cm compared to that of the RF model which was equal to 0.4906 cm. MAE for the XGBoost model was 0.1217 cm and for the RF model was 0.3050 cm. Therefore, the achieved results proved that XGBoost had better performance in this research for predicting LS values based on the measured ones.
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Wang, Ming, Dehua Mao, Yeqiao Wang, Kaishan Song, Hengqi Yan, Mingming Jia, and Zongming Wang. "Annual Wetland Mapping in Metropolis by Temporal Sample Migration and Random Forest Classification with Time Series Landsat Data and Google Earth Engine." Remote Sensing 14, no. 13 (July 2, 2022): 3191. http://dx.doi.org/10.3390/rs14133191.
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Wetlands provide various ecosystem services to urban areas, which are crucial for sustainable urban management. With intensified urbanization, there has been marked loss of urban natural wetland, degradation, and related urban disasters in the past several decades. Rapid and accurate mapping of urban wetland extent and change is thus critical for improving urban planning toward sustainability. Here, we have developed a rapid method for continuous mapping of urban wetlands (MUW) by combining automatic sample migration and the random forest algorithm (SM&RF), the so-called MUW_SM&RF. Using time series Landsat images, annual training samples were generated through spectral angular distance (SAD) and time series analysis. Combined with the RF algorithm, annual wetland maps in urban areas were derived. Employing the Google Earth Engine platform (GEE), the MUW_SM&RF was evaluated in four metropolitan areas in different geographical and climatic regions of China from 1990 to 2020, including Tianjin, Hangzhou, Guangzhou, and Wuhan. In all four study areas, the generated annual wetland maps had an overall accuracy of over 87% and a Kappa coefficient above 0.815. Compared with previously published datasets, the urban wetland areas derived using the MUW_SM&RF approach achieved improved accuracy and thus demonstrated its robustness for rapid mapping of urban wetlands. Urban wetlands in all four cities had variable distribution patterns and showed significantly decreased trends in the past three decades. The annual urban wetland data product generated by the MUW_SM&RF can provide invaluable information for sustainable urban planning and management, so as for assessment related to the United Nation’s sustainable development goals.
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Jiang, Fugen, Andrew R. Smith, Mykola Kutia, Guangxing Wang, Hua Liu, and Hua Sun. "A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China." Remote Sensing 12, no. 11 (June 10, 2020): 1884. http://dx.doi.org/10.3390/rs12111884.
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As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of the vegetation canopy. Here, a novel and modified kNN method was presented for mapping LAI in arid and semi-arid areas of China using Sentinel-2 and Landsat 8 images with field data collected in Ganzhou and Kangbao of China. The modified kNN was developed by integrating the traditional kNN estimation and RF classification. The results were compared with those from kNN and RF regression alone using three sets of input predictors: (i) spectral reflectance bands (input 1); (ii) vegetation indices (input 2); and (iii) a combination of spectral reflectance bands and vegetation indices (input 3). Our analysis showed that in Ganzhou, the red-edge bands of the Sentinel-2 image had a high correlation with LAI. Using the red-edge band-derived vegetation indices increased the accuracy of mapping LAI compared with using other spectral variables. Among the three sets of input predictors, input 3 resulted in the highest prediction accuracy. Based on the combination, the values of RMSE obtained by the traditional kNN, RF, and modified kNN were 0.526, 0.523, and 0.372, respectively, and the modified kNN significantly improved the accuracy of LAI prediction by 29.3% and 28.9% compared with the kNN and RF alone, respectively. A similar improvement was achieved for input 1 and input 2. In Kangbao, the improvement of the prediction accuracy obtained by the modified kNN was 31.4% compared with both the kNN and RF. Therefore, this study implied that the modified kNN provided the potential to improve the accuracy of mapping LAI in arid and semi-arid regions using the images.
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Tsogtbaatar, Undrakhtsetseg, Sainbayar Dalantai, and Bayartungalag Batsaikhan. "Soil moisture mapping using machine learning technique." Mongolian Journal of Geography and Geoecology 60, no. 44 (December 28, 2023): 222–30. http://dx.doi.org/10.5564/mjgg.v60i44.3062.
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Soil moisture is an essential component in the energy cycle, water resource, hydrological regime, and processes of the land surface. Mapping and monitoring of soil moisture are crucial for the prevention of flood and drought, estimation of evapotranspiration, and water resource management. Using remote sensing to create soil moisture mapping at large scale has become one of the most energy and time-efficient methods in soil study. Thus, we aimed to map the soil moisture for Mongolia based on downscaled Soil Moisture Active Passive (SMAP) data by combining it with the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Temperature (LST) of Moderate Resolution Imaging Spectroradiometer (MODIS) data using the Machine Learning-based Random Forest (RF) approach. The SMAP was positively correlated with NDVI (r=0.72, p<0.01) and EVI (r=0.73, p<0.01) but it was negatively correlated with LST (r= -0.66, p<0.05). The performance of the RF was high, and the correlation was r2=0.7. Therefore, our study suggests that the Machine Learning-based RF approach can be used to model soil moisture on a large scale. Машин сургалтын аргаар хөрсний чийгийг зураглах арга зүй Хөрсний чийг нь усны эргэлт, энергийн урсгалд чухал нөлөө үзүүлдгээс гадна, газрын гадаргын нөхцөл болон гадаргын усанд маш чухал нөлөөтэй. Иймд, хөрсний чийгийн зураглал болон мониторингийн судалгаа нь ган, зудын мониторинг, үерийн урьдчилсан сэрэмжлүүлэг болон усны нөөцийн менежментэд чухал үүрэг гүйцэтгэдэг судалгааны нэг юм. Сүүлийн үед, өргөн уудам газар нутагт хөрсний чийгийг зураглахын тулд зайнаас тандан судлалын аргыг ашиглах нь эдийн засаг болон цаг хугацааны хувьд үр ашигтай аргуудын нэг болоод байна. Иймд Монгол орны хэмжээнд хөрсний чийгийг зураглахдаа Soil Moisture Active Passive (SMAP) хиймэл дагуулын бүтээгдэхүүнийг ашиглан машин сургалтын санамсаргүй ой (RF)-н аргаар мэдээний орон зайн шийдийг сайжруулан зураглалаа. Ингэхдээ Moderate Resolution Imaging Spectroradiometer (MODIS) хиймэл дагуулын бүтээгдэхүүнүүдэд (ургамлын нормчилсон ялгаврын индекс (NDVI), ургамлын сайжруулсан индекс (EVI), газрын гадаргын температур (LST) тулгуурлан SMAP хиймэл дагуулын бүтээгдэхүүний орон зайн шийдийг сайжруулан өөрчилсөн, хамаарлыг тооцсон. Ингэхэд NDVI (r=0.72, p<0.01) болон EVI (r=0.73, p<0.01) нь SMAP-тай эерэг хамааралтай байсан бол LST (r= -0.66, p<0.05)-тай урвуу хамааралтай байсан. RF-н алгоритмаар машин сургалтын аргыг ашиглан Монгол орны хэмжээнд хөрсний чийгийг зураглахад загварын үр дүн гүйцэтгэл сайтай буюу хамаарал нь r2=0.7 гарсан. Иймд машин сургалтын санамсаргүй ойн алгоритмаар том хэмжээний газар нутгийг хамруулан хөрсний чийгийг загварчлах боломжтой болох нь судалгааны үр дүнгээс харагдаж байна. Түлхүүр үгс: Хөрсний чийг, машин сургалт, SMAP
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Wang, Tiebin, Kaiyuan Huang, Min Liu, and Ranran He. "Sparse Space Shift Keying Modulation with Enhanced Constellation Mapping." Sensors 22, no. 15 (August 7, 2022): 5895. http://dx.doi.org/10.3390/s22155895.
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For reducing the switching frequency between the radio frequency (RF) chain and transmit antennas, a class of new sparse space shift keying modulation (SSSK) schemes are presented. This new class is proposed to simplify hardware implementation, through carefully designing the spatial constellation mapping pattern. Specifically, different from traditional space shift keying modulation (SSK), the proposed SSSK scheme utilizes more time slots to construct a joint design of time and spatial domain SSK modulation, while maintaining the special structure of single RF chain. Since part of the multi-dimension constellations of SSSK concentrate the energy in less time slots, the RF-switching frequency is effectively reduced due to the sparsity introduced in the time domain. Furthermore, through theoretical analysis, we obtain the closed-form expression of the bit error probability for the SSSK scheme, and demonstrate that slight performance gain can be achieved compared to traditional SSK with reduced implementation cost. Moreover, we integrate transmit antenna selection (TAS) to achieve considerable performance gain. Finally, simulation results confirm the effectiveness of the proposed SSSK scheme compared to its traditional counterpart.
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Ganjirad, M., and M. R. Delavar. "FLOOD RISK MAPPING USING RANDOM FOREST AND SUPPORT VECTOR MACHINE." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 13, 2023): 201–8. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-201-2023.
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Abstract. Floods are among the natural disasters that cause financial and human losses all over the world every year. By production of a flood risk map and determination of potential flood risk areas, the possible damages of this phenomenon can be reduced. To map the flood extend in Calcasieu Parish, Louisiana, US, conditioning factors affecting the flood occurrence including elevation, slope, plan curvature, land use, distance from rivers, density of rivers, rainfall, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and normalized difference built-up index (NDBI) were identified and their information layers produced using the Google Earth Engine (GEE) cloud platform. Then, for flood risk mapping, Random Forest (RF) and support vector machine (SVM) as two machine learning models have been implemented and their results compared. RF and SVM models have been validated based on the maximum absolute error (MAE) index with an accuracy of 0.043 and 0.097, respectively. Visualization of the predicted values in QGIS software confirms that the RF model has provided better outputs than that of the SVM model. By analysing the features importance of the layers in the RF model, it was verified that the elevation, slope, and plan curvature layers have the highest degree of influence on the flood risk with degrees of importance of 0.197, 0.135, and 0.123.
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Yang, Zhicheng, Andrea D’Alpaos, Marco Marani, and Sonia Silvestri. "Assessing the Fractional Abundance of Highly Mixed Salt-Marsh Vegetation Using Random Forest Soft Classification." Remote Sensing 12, no. 19 (October 3, 2020): 3224. http://dx.doi.org/10.3390/rs12193224.
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Coastal salt marshes are valuable and critical components of tidal landscapes, currently threatened by increasing rates of sea level rise, wave-induced lateral erosion, decreasing sediment supply, and human pressure. Halophytic vegetation plays an important role in salt-marsh erosional and depositional patterns and marsh survival. Mapping salt-marsh halophytic vegetation species and their fractional abundance within plant associations can provide important information on marsh vulnerability and coastal management. Remote sensing has often provided valuable methods for salt-marsh vegetation mapping; however, it has seldom been used to assess the fractional abundance of halophytes. In this study, we developed and tested a novel approach to estimate fractional abundance of halophytic species and bare soil that is based on Random Forest (RF) soft classification. This approach can fully use the information contained in the frequency of decision tree “votes” to estimate fractional abundance of each species. Such a method was applied to WorldView-2 (WV-2) data acquired for the Venice lagoon (Italy), where marshes are characterized by a high diversity of vegetation species. The proposed method was successfully tested against field observations derived from ancillary field surveys. Our results show that the new approach allows one to obtain high accuracy (6.7% < root-mean-square error (RMSE) < 18.7% and 0.65 < R2 < 0.96) in estimating the sub-pixel fractional abundance of marsh-vegetation species. Comparing results obtained with the new RF soft-classification approach with those obtained using the traditional RF regression method for fractional abundance estimation, we find a superior performance of the novel RF soft-classification approach with respect to the existing RF regression methods. The distribution of the dominant species obtained from the RF soft classification was compared to the one obtained from an RF hard classification, showing that numerous mixed areas are wrongly labeled as populated by specific species by the hard classifier. As for the effectiveness of using WV-2 for salt-marsh vegetation mapping, feature importance analyses suggest that Yellow (584–632 nm), NIR 1 (near-infrared 1, 765–901 nm) and NIR 2 (near-infrared 2, 856–1043 nm) bands are critical in RF soft classification. Our results bear important consequences for mapping and monitoring vegetation-species fractional abundance within plant associations and their dynamics, which are key aspects in biogeomorphic analyses of salt-marsh landscapes.
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Han, Hyangsun, Taewook Kim, and Seohyeon Kim. "River Ice Mapping from Landsat-8 OLI Top of Atmosphere Reflectance Data by Addressing Atmospheric Influences with Random Forest: A Case Study on the Han River in South Korea." Remote Sensing 16, no. 17 (August 29, 2024): 3187. http://dx.doi.org/10.3390/rs16173187.
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Accurate river ice mapping is crucial for predicting and managing floods caused by ice jams and for the safe operation of hydropower and water resource facilities. Although satellite multispectral images are widely used for river ice mapping, atmospheric contamination limits their effectiveness. This study developed river ice mapping models for the Han River in South Korea using atmospherically uncorrected Landsat-8 Operational Land Imager (OLI) multispectral reflectance data, addressing atmospheric influences with a Random Forest (RF) classification approach. The RF-based river ice mapping models were developed by implementing various combinations of input variables, incorporating the Landsat-8 multispectral top-of-atmosphere (TOA) reflectance, normalized difference indices for snow, water, and bare ice, and atmospheric factors such as aerosol optical depth, water vapor content, and ozone concentration from the Moderate Resolution Imaging Spectroradiometer observations, as well as surface elevation from the GLO-30 digital elevation model. The RF model developed using all variables achieved excellent performance in the classification of snow-covered ice, snow-free ice, and water, with an overall accuracy and kappa coefficient exceeding 98.4% and 0.98 for test samples, and higher than 83.7% and 0.75 when compared against reference river ice maps generated by manually interpreting the Landsat-8 images under various atmospheric conditions. The RF-based river ice mapping model for the atmospherically corrected Landsat-8 multispectral surface reflectance was also developed, but it showed very low performance under atmospheric conditions heavily contaminated by aerosol and water vapor. Aerosol optical depth and water vapor content were identified as the most important variables. This study demonstrates that multispectral reflectance data, despite atmospheric contamination, can be effectively used for river ice monitoring by applying machine learning with atmospheric auxiliary data to mitigate atmospheric effects.
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Xi, Jing, Qigang Jiang, Huaxin Liu, and Xin Gao. "Lithological Mapping Research Based on Feature Selection Model of ReliefF-RF." Applied Sciences 13, no. 20 (October 12, 2023): 11225. http://dx.doi.org/10.3390/app132011225.
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Multispectral satellite data allow experts to discriminate rock units based on their spectral signature characteristics. Here, Sentinel-2, ASTER and the Landsat-8 Operational Land Imager (OLI) were assessed for lithological mapping by using a random forest (RF) classifier for a study area located in Xitieshan, Northwest China. The classification accuracy of Sentinel-2 was 60.71%, which was 5.24% and 4.77% higher than the accuracies for ASTER and the Landsat-8 OLI, respectively. Three image enhancement techniques, namely, principal component analysis (PCA), independent component analysis (ICA) and minimum noise fraction (MNF), were used with grey-level cooccurrence matrices (GLCMs) to increase the quality of the input datasets. The ICA could discriminate between rock unit datasets better than the other approaches. In contrast, GLCM performed poorly when used independently. The overall classification accuracies were 60.71%, 62.63%, 64.34%, 65.21% and 58.87% for the 10 bands of Sentinel-2, PCA, MNF, ICA and GLCM, respectively. Then, five datasets were combined as a single group and applied in RF classification. Sentinel-2 obtained an overall accuracy of 73.96% and performed better than the other single-dataset approaches used in this study. Furthermore, the classification result of RF was achieved better performance than that of the support vector machine algorithm (SVM). During feature selection processing, ReliefF, the most successful pre-processing algorithm, was employed to preliminarily perform feature screening. Then, the optimal dataset was selected on the basis of the importance ranking of RF. A total of 20 more important predictors were selected from 114 original features using the ReliefF-RF model. These predictors were used in the lithological mapping, and an overall accuracy of 77.63% was reached.
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Tan, X. L. "Mapping of Rice Rf Gene by Bulked Line Analysis." DNA Research 5, no. 1 (January 1, 1998): 15–18. http://dx.doi.org/10.1093/dnares/5.1.15.
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Shapiro, Erik M., Arijitt Borthakur, Michael J. Shapiro, Ravinder Reddy, and John S. Leigh. "Fast MRI of RF heating via phase difference mapping." Magnetic Resonance in Medicine 47, no. 3 (February 20, 2002): 492–98. http://dx.doi.org/10.1002/mrm.10067.
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Khalighi, Mohammad Mehdi, Brian K. Rutt, and Adam B. Kerr. "RF pulse optimization for Bloch-Siegert B +1 mapping." Magnetic Resonance in Medicine 68, no. 3 (December 5, 2011): 857–62. http://dx.doi.org/10.1002/mrm.23271.
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Okoh, Supreme Ayewoh, Stanley Tio Andeobu, John-Davis Chukwuemeka Oyedum, Nuhu Sadiq, and Elizabeth Nonye Onwuka. "RF Coverage Mapping of Mobile Phone Networks in Nigeria." International Journal of Computing and Digital Systems 12, no. 1 (July 20, 2022): 417–25. http://dx.doi.org/10.12785/ijcds/120133.
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Cetinkaya, S., and S. Kocaman. "SNOW AVALANCHE SUSCEPTIBILITY MAPPING FOR DAVOS, SWITZERLAND." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 1083–90. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1083-2022.
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Abstract. Snow avalanches are among destructive hazards occurring in mountainous regions and spatial distribution (susceptibility) of their occurrences needs to be considered for spatial planning and disaster risk mitigation efforts. The susceptibility assessment is the first step in avalanche disaster management and can be carried out using high resolution geospatial data and machine learning (ML) algorithms. In this study, we have assessed the snow avalanche susceptibility in Davos, Switzerland using an inventory delineated on satellite imagery in a previous study. The conditioning factors used for the avalanche susceptibility assessment include elevation, slope, plan curvature, profile curvature, aspect, topographic position index, topographic ruggedness index, topographic wetness index, land use and land cover, lithology, distance to road, and distance to the river. Two ML algorithms, the logistic regression (LR) and the random forest (RF), were comparatively assessed using validation data split from the training data (30/70). The prediction performances of both models were assessed based on the area under the receiver operating characteristic curve (ROC-AUC) value. Although the AUC value obtained from the LR method was relatively low (0.74), the value obtained from the RF (0.96) demonstrated high performance and usability of this approach. The results indicate that the RF method can successfully produce an avalanche susceptibility map for the region, although potential improvements may be possible by investigating various input features and ML algorithms as well as by classifying the starting and runout zones of the avalanche data separately. Furthermore, the accuracy is expected to increase by using a larger training dataset.
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Alzahrani, Ali, and Awos Kanan. "Machine Learning Approaches for Developing Land Cover Mapping." Applied Bionics and Biomechanics 2022 (June 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/5190193.
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In remote sensing data processing, cover classification on decimeter-level data is a well-studied but tough subject that has been well-documented. The majority of currently existent works make use of orthographic photographs or orthophotos and digital surface models that go with them (DSMs). Urban land cover classification plays a significant role in the field of remote sensing to enhance the quality of different applications including environment protection, sustainable development, and resource management and planning. Novelty of the research done in this area is focused on extracting features from high-resolution satellite images to be used in the classification process. However, it is well known in machine learning literature that some of the extracted features are irrelevant to the classification process with a negative or no effect on its accuracy. In this work, a genetic algorithm-based feature selection approach is used to enhance the performance of urban land cover classification. Neural networks (NNs) and random forest (RF) classifiers were used to evaluate the proposed approach on a recent urban land cover dataset of nine different classes. Experimental results show that the proposed approach achieved better performance with RF classifier using only 27% of the features. The random forest tree has achieved highest accuracy 84.27%; it is concluded that the RF algorithm is an appropriate algorithm for classifying cover land.
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Sarkar, Showmitra Kumar, Swapan Talukdar, Atiqur Rahman, Shahfahad, and Sujit Kumar Roy. "Groundwater potentiality mapping using ensemble machine learning algorithms for sustainable groundwater management." Frontiers in Engineering and Built Environment 2, no. 1 (November 2, 2021): 43–54. http://dx.doi.org/10.1108/febe-09-2021-0044.
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PurposeThe present study aims to construct ensemble machine learning (EML) algorithms for groundwater potentiality mapping (GPM) in the Teesta River basin of Bangladesh, including random forest (RF) and random subspace (RSS).Design/methodology/approachThe RF and RSS models have been implemented for integrating 14 selected groundwater condition parametres with groundwater inventories for generating GPMs. The GPM were then validated using the empirical and bionormal receiver operating characteristics (ROC) curve.FindingsThe very high (831–1200 km2) and high groundwater potential areas (521–680 km2) were predicted using EML algorithms. The RSS (AUC-0.892) model outperformed RF model based on ROC's area under curve (AUC).Originality/valueTwo new EML models have been constructed for GPM. These findings will aid in proposing sustainable water resource management plans.
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Lin, Chinsu, and Nova D. Doyog. "Challenges of Retrieving LULC Information in Rural-Forest Mosaic Landscapes Using Random Forest Technique." Forests 14, no. 4 (April 15, 2023): 816. http://dx.doi.org/10.3390/f14040816.
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Land use and land cover (LULC) information plays a crucial role in determining the trend of the global carbon cycle in various fields, such as urban land planning, agriculture, rural management, and sustainable development, and serves as an up-to-date indicator of forest changes. Accurate and reliable LULC information is needed to address the detailed changes in conservation-based and development-based classes. This study integrates Sentinel-2 multispectral surface reflectance and vegetation indices, and lidar-based canopy height and slope to generate a random forest model for 3-level LULC classification. The challenges for LULC classification by RF approach are discussed by comparing it with the SVM model. To summarize, the RF model achieved an overall accuracy (OA) of 0.79 and a macro F1-score of 0.72 for the Level-III classification. In contrast, the SVM model outperformed the RF model by 0.04 and 0.09 in OA and macro F1-score, respectively. The accuracy difference increased to 0.89 vs. 0.96 for OA and 0.79 vs. 0.91 for macro F1-score for the Level-I classification. The mapping reliability of the RF model for different classes with nearly identical features was challenging with regard to precision and recall measures which are both inconsistent in the RF model. Therefore, further research is needed to close the knowledge gap associated with reliable and high thematic LULC mapping using the RF classifier.
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Pavanelli, João Arthur Pompeu, João Roberto dos Santos, Lênio Soares Galvão, Maristela Xaud, and Haron Abrahim Magalhães Xaud. "PALSAR-2/ALOS-2 AND OLI/LANDSAT-8 DATA INTEGRATION FOR LAND USE AND LAND COVER MAPPING IN NORTHERN BRAZILIAN AMAZON." Boletim de Ciências Geodésicas 24, no. 2 (June 2018): 250–69. http://dx.doi.org/10.1590/s1982-21702018000200017.
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Abstract: In northern Brazilian Amazon, the crops, savannahs and rainforests form a complex landscape where land use and land cover (LULC) mapping is difficult. Here, data from the Operational Land Imager (OLI)/Landsat-8 and Phased Array type L-band Synthetic Aperture Radar (PALSAR-2)/ALOS-2 were combined for mapping 17 LULC classes using Random Forest (RF) during the dry season. The potential thematic accuracy of each dataset was assessed and compared with results of the hybrid classification from both datasets. The results showed that the combination of PALSAR-2 HH/HV amplitudes with the reflectance of the six OLI bands produced an overall accuracy of 83% and a Kappa of 0.81, which represented an improvement of 6% in relation to the RF classification derived solely from OLI data. The RF models using OLI multispectral metrics performed better than RF models using PALSAR-2 L-band dual polarization attributes. However, the major contribution of PALSAR-2 in the savannahs was to discriminate low biomass classes such as savannah grassland and wooded savannah.
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Shao, Yakui, Zhongke Feng, Meng Cao, Wenbiao Wang, Linhao Sun, Xuanhan Yang, Tiantian Ma, et al. "An Ensemble Model for Forest Fire Occurrence Mapping in China." Forests 14, no. 4 (March 29, 2023): 704. http://dx.doi.org/10.3390/f14040704.
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Assessing and predicting forest fires has long been an arduous task. Nowadays, the rapid advancement of artificial intelligence and machine learning technologies have provided a novel solution to forest fire occurrence assessment and prediction. In this research, we developed a novel hybrid machine-learning-technique algorithm to improve forest fire prediction based on random forest (RF), gradient-boosting decision tree (GBDT), support vector machine (SVM), and other machine learning models. The dataset we employed was satellite fire point data from 2010 to 2018 from the Chinese Department of Fire Prevention. The efficacy and performance of our methods were examined by validating the model fit and predictive capability. The results showed that the ensemble model LR (logistic regression)-RF-SVM-GBDT outperformed the single RFSVMGBDT model and the LR-RF-GBDT integrated framework, displaying higher accuracy and greater robustness. We believe that our newly developed hybrid machine-learning algorithm has the potential to improve the accuracy of predicting forest fire occurrences, thus enabling more efficient firefighting efforts and saving time and resources.
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Wang, Changpeng, Yangchun Lin, Zhiwen Tao, Jiayin Zhan, Wenkai Li, and Huabing Huang. "An Inverse-Occurrence Sampling Approach for Urban Flood Susceptibility Mapping." Remote Sensing 15, no. 22 (November 16, 2023): 5384. http://dx.doi.org/10.3390/rs15225384.
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Data-driven flood susceptibility modeling is an efficient way to map the spatial distribution of flood likelihood. The quality of the flood susceptibility model relies on the learning technique and the data used for learning. The performance of learning techniques has been extensively examined. However, to date, the impact of data sampling strategies has received limited attention. Random sampling is widely favored because of its ease of use. It treats flood-related data as tabular and excludes their spatial dimensions. Flood occurrence is typically uneven over space. Therefore, non-flood sampling should not be completely random. To represent the impact of the spatial dimension, this study proposed a new sampling approach based on spatial dependence, called inverse-occurrence sampling. It selects more non-flood data in low-risk areas than in high-risk areas. The new sampling approach was compared with random and stratified sampling, using six machine learning techniques in two urban areas in Guangzhou, China, with distinct flood mechanisms, that is, Tianhe (flood density 1.5/km2, clustered distribution, average slope 9.02°, downtown district) and Panyu (flood density 0.15/km2, random distribution, average slope 4.55°, suburban district). Learning techniques include support vector machine (SVM), random forest (RF), artificial neural networks (ANNs), convolutional neural networks (CNNs), CNN-SVM, and CNN-RF. The main findings of this study were as follows: (1) Sampling approaches had a greater impact on model performance than learning techniques in terms of area under the receiver operating characteristic curve (AUC). The AUC variations caused by learning techniques ranged from 0.04 to 0.09. Meanwhile, the AUC variations caused by sampling approaches were between 0.15 and 0.22, all larger than 0.1. (2) The new sampling approach outperformed that of the other two sampling approaches for high average AUC values and small AUC variations. The outperformance is robust in regard to multiple learning techniques and different flooding mechanisms. AUCs in the inverse group had a narrower range (0.14–0.18 in Tianhe and 0.35–0.39 in Panyu) than in the random group (0.24–0.28 in Tianhe and 0.43–0.53 in Panyu) and the stratified group (0.23–0.30 in Tianhe and 0.42–0.48 in Panyu). (3) The most accurate learning technique for AUC was CNN-RF, followed by SVM, CNN-SVM, RF, CNN, and ANN. (4) ANN- and CNN-based models tended to produce polarized patterns in flood susceptibility maps, contradicting the ascending order of flood density with increasing susceptibility levels. Flood density outliers tended to appear in the models derived using RF and CNN-RF. Finally, the newly proposed sampling approach is suggested to be applied to flood susceptibility mapping to reflect the impact of spatial dependence.
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Lachaud, Alix, Marcus Adam, and Ilija Mišković. "Comparative Study of Random Forest and Support Vector Machine Algorithms in Mineral Prospectivity Mapping with Limited Training Data." Minerals 13, no. 8 (August 13, 2023): 1073. http://dx.doi.org/10.3390/min13081073.
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This paper employs two data-driven methods, Random Forest (RF) and Support Vector Machines (SVM), to develop mineral prospectivity models for an epithermal Au deposit. Four distinct models are presented for comparison: one employing RF and three using SVM with different kernel functions—namely linear, Radial Basis Function (RBF), and polynomial. The analysis leverages a compact training dataset, encompassing just 20 deposits, with deposit and non-deposit locations chosen from known mineral occurrences. Fourteen predictor maps are constructed based on the available data and the exploration model. The findings indicate that RF is more stable and robust than SVM, regardless of the kernel function implemented. While all SVM models outperformed the RF model in terms of classification capability on the training dataset achieving an accuracy exceeding 89% versus 78% for the RF model, the success rate curves suggest superior predictive abilities of RF over SVM models. This implies that the SVM models may be overfitting the training data due to the limited quantity of training deposits.
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Wu, Fan, Yufen Ren, and Xiaoke Wang. "Application of Multi-Source Data for Mapping Plantation Based on Random Forest Algorithm in North China." Remote Sensing 14, no. 19 (October 3, 2022): 4946. http://dx.doi.org/10.3390/rs14194946.
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The expansion of plantation poses new challenges for mapping forest, especially in mountainous regions. Using multi-source data, this study explored the capability of the random forest (RF) algorithm for the extraction and mapping of five forest types located in Yanqing, north China. The Google Earth imagery, forest inventory data, GaoFen-1 wide-field-of-view (GF-1 WFV) images and DEM were applied for obtaining 125 features in total. The recursive feature elimination (RFE) method selected 32 features for mapping five forest types. The results attained overall accuracy of 87.06%, with a Kappa coefficient of 0.833. The mean decrease accuracy (MDA) reveals that the DEM, LAI and EVI in winter and three texture features (entropy, variance and mean) make great contributions to forest classification. The texture features from the NIR band are important, while the other texture features have little contribution. This study has demonstrated the potential of applying multi-source data based on RF algorithm for extracting and mapping plantation forest in north China.
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Van Huynh, Chuong, Tung Gia Pham, Linh Hoang Khanh Nguyen, Hai Trung Nguyen, Phuong Thuy Nguyen, Quy Ngoc Phuong Le, Phuong Thị Tran, Mai Thi Hong Nguyen, and Tuyet Thi Anh Tran. "Application GIS and remote sensing for soil organic carbon mapping in a farm-scale in the hilly area of central Vietnam." Air, Soil and Water Research 15 (January 2022): 117862212211147. http://dx.doi.org/10.1177/11786221221114777.
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Soil Organic Carbon (SOC) influences many soil properties including nutrient and water holding capacity, nutrient cycling and stability, improved water infiltration and aeration. It also is an essential parameter in the assessment of soil quality, especially for agricultural production. However, SOC mapping is a complicated process that is costly and time-consuming due to the physical challenges of the natural conditions that is being surveyed. The best model for SOC mapping is still in debate among many researchers. Recently, the development of machine learning and Geographical Information Systems (GIS) has provided the potential for more accurate spatial prediction of SOC content. This research was conducted in a relatively small-scale capacity in the Central Vietnam region. The aim of this study is to compare the accuracy of Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Random Forest (RF) methods for SOC interpolation, with a dataset of 47 soil samples for an area of 145 hectares. Three environmental variables including elevation, slope, and the Normalized Difference Vegetation Index (NDVI) were used for the RF model. In the RF model, the values of the number of variables randomly sampled as candidates at each split, (mtry), and the number of bootstrap replicates, (ntree), were determined in terms of 1 and 1,000 respectively The results at our research site showed that using IDW is the most accurate method for SOC mapping, followed by the methods of RF and OK respectively. Concerning SOC mapping based-on auxiliary variables, in areas where there is human activity, the selection of auxiliary variables should be carefully considered because the variation in the SOC may not only be due to environmental variables but also by farming technologies.
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Berangi, Mostafa, Andre Kuehne, Helmar Waiczies, and Thoralf Niendorf. "MRI of Implantation Sites Using Parallel Transmission of an Optimized Radiofrequency Excitation Vector." Tomography 9, no. 2 (March 8, 2023): 603–20. http://dx.doi.org/10.3390/tomography9020049.
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Postoperative care of orthopedic implants is aided by imaging to assess the healing process and the implant status. MRI of implantation sites might be compromised by radiofrequency (RF) heating and RF transmission field (B1+) inhomogeneities induced by electrically conducting implants. This study examines the applicability of safe and B1+-distortion-free MRI of implantation sites using optimized parallel RF field transmission (pTx) based on a multi-objective genetic algorithm (GA). Electromagnetic field simulations were performed for eight eight-channel RF array configurations (f = 297.2 MHz), and the most efficient array was manufactured for phantom experiments at 7.0 T. Circular polarization (CP) and orthogonal projection (OP) algorithms were applied for benchmarking the GA-based shimming. B1+ mapping and MR thermometry and imaging were performed using phantoms mimicking muscle containing conductive implants. The local SAR10g of the entire phantom in GA was 12% and 43.8% less than the CP and OP, respectively. Experimental temperature mapping using the CP yielded ΔT = 2.5–3.0 K, whereas the GA induced no extra heating. GA-based shimming eliminated B1+ artefacts at implantation sites and enabled uniform gradient-echo MRI. To conclude, parallel RF transmission with GA-based excitation vectors provides a technical foundation en route to safe and B1+-distortion-free MRI of implantation sites.
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Ouma, Yashon O., Thabiso G. Gabasiane, and Nyaladzani Nkhwanana. "Mapping Prosopis L. (Mesquites) Using Sentinel-2 MSI Satellite Data, NDVI and SVI Spectral Indices with Maximum-Likelihood and Random Forest Classifiers." Journal of Sensors 2023 (July 4, 2023): 1–18. http://dx.doi.org/10.1155/2023/8882730.
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Mapping of invasive alien plants (IAPs) is important for developing informed initiatives to assist environmentalists in managing the spread and impacts of IAPs. The Prosopis plant species is an aggressive IAP that has been considered a scourge in different regions of the globe. The aim of this study is to map the spatial distribution of the invasive alien Prosopis plant in southwestern Botswana using the higher spatial and spectral resolution Sentinel-2A (S2A) MultiSpectral Instrument (MSI) satellite sensor data. Supervised parametric maximum likelihood classification (MLC) was compared with the nonparametric Random Forest (RF) classifier for the detection and mapping of the Prosopis using 10 m S2A sensor bands, integrated with normalized difference vegetation index (NDVI) and Sentinel Improved Vegetation Index (SVI). Using S2A, S2A and NDVI, and S2A and SVI, MLC mapped the land use/land cover (LULC) in the study area with respective accuracies of 71.5%, 66.5%, and 79.9%, while RF mapped the LULC with accuracies of 93.2%, 77.3%, and 95.6%. Using RF, S2A multispectral data and the red edge wavelength-based SVI were found to be more suitable for mapping the distribution of Prosopis with classification accuracy of 18.3% higher than for NDVI. The study findings can be used by environmentalists, policy, and decision makers in the context of mapping, monitoring, and management of the invasive Prosopis.
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Silva, Sérgio Henrique Godinho, Anita Fernanda dos Santos Teixeira, Michele Duarte de Menezes, Luiz Roberto Guimarães Guilherme, Fatima Maria de Souza Moreira, and Nilton Curi. "Multiple linear regression and random forest to predict and map soil properties using data from portable X-ray fluorescence spectrometer (pXRF)." Ciência e Agrotecnologia 41, no. 6 (December 2017): 648–64. http://dx.doi.org/10.1590/1413-70542017416010317.
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ABSTRACT Determination of soil properties helps in the correct management of soil fertility. The portable X-ray fluorescence spectrometer (pXRF) has been recently adopted to determine total chemical element contents in soils, allowing soil property inferences. However, these studies are still scarce in Brazil and other countries. The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil properties. 120 soil samples were collected at three depths and submitted to laboratory analyses. pXRF was used in the samples and total element contents were determined. From pXRF data, SMLR and RF were used to predict soil laboratory results, reflecting soil properties, and the models were validated. The best method was used to spatialize soil properties. Using SMLR, models had high values of R² (≥0.8), however the highest accuracy was obtained in RF modeling. Exchangeable Ca, Al, Mg, potential and effective cation exchange capacity, soil organic matter, pH, and base saturation had adequate adjustment and accurate predictions with RF. Eight out of the 10 soil properties predicted by RF using pXRF data had CaO as the most important variable helping predictions, followed by P2O5, Zn and Cr. Maps generated using RF from pXRF data had high accuracy for six soil properties, reaching R2 up to 0.83. pXRF in association with RF can be used to predict soil properties with high accuracy at low cost and time, besides providing variables aiding digital soil mapping.
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Forsey, D., B. Leblon, A. LaRocque, M. Skinner, and A. Douglas. "EELGRASS MAPPING IN ATLANTIC CANADA USING WORLDVIEW-2 IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 685–92. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-685-2020.
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Abstract. Eelgrass (Zostera marina L.) is a marine angiosperm plant that grows throughout coastal areas in Atlantic Canada. Eelgrass meadows provide numerous ecosystem services, and while they have been acknowledged as important habitats, their location, extent, and health in Atlantic Canada are poorly understood. This study examined the effectiveness of WorldView-2 optical satellite imagery to map eelgrass presence in Tabusintac Bay, New Brunswick (Canada), an estuarine lagoon with extensive eelgrass coverage. The imagery was classified using two supervised classifiers: the parametric Maximum Likelihood Classifier (MLC) and the non-parametric Random Forests (RF) classifier. While Random Forests was expected to produce higher classification accuracies, it was shown not to be much better than MLC. The overall validation accuracy was 97.6% with RF and 99.8% with MLC.
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Bazzi, Hassan, Nicolas Baghdadi, Dino Ienco, Mohammad El Hajj, Mehrez Zribi, Hatem Belhouchette, Maria Jose Escorihuela, and Valérie Demarez. "Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain." Remote Sensing 11, no. 15 (August 6, 2019): 1836. http://dx.doi.org/10.3390/rs11151836.
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Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.
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Song, Weicheng, Aiqing Feng, Guojie Wang, Qixia Zhang, Wen Dai, Xikun Wei, Yifan Hu, Solomon Obiri Yeboah Amankwah, Feihong Zhou, and Yi Liu. "Bi-Objective Crop Mapping from Sentinel-2 Images Based on Multiple Deep Learning Networks." Remote Sensing 15, no. 13 (July 6, 2023): 3417. http://dx.doi.org/10.3390/rs15133417.
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Accurate assessment of the extent of crop distribution and mapping different crop types are essential for monitoring and managing modern agriculture. Medium and high spatial resolution remote sensing (RS) for Earth observation and deep learning (DL) constitute one of the most major and effective tools for crop mapping. In this study, we used high-resolution Sentinel-2 imagery from Google Earth Engine (GEE) to map paddy rice and winter wheat in the Bengbu city of Anhui Province, China. We compared the performance of different popular DL backbone networks with the traditional machine learning (ML) methods, including HRNet, MobileNet, Xception, and Swin Transformer, within the improved DeepLabv3+ architecture, Segformer and random forest (RF). The results showed that the Segformer based on the combination of the Transformer architecture encoder and the lightweight multilayer perceptron (MLP) decoder achieved an overall accuracy (OA) value of 91.06%, a mean F1 Score (mF1) value of 89.26% and a mean Intersection over Union (mIoU) value of 80.70%. The Segformer outperformed other DL methods by combining the results of multiple evaluation metrics. Except for Swin Transformer, which was slightly lower than RF in OA, all DL methods significantly outperformed RF methods in accuracy for the main mapping objects, with mIoU improving by about 13.5~26%. The predicted images of paddy rice and winter wheat from the Segformer were characterized by high mapping accuracy, clear field edges, distinct detail features and a low false classification rate. Consequently, DL is an efficient option for fast and accurate mapping of paddy rice and winter wheat based on RS imagery.
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Kumar, Suchit, Kyu Chan Lee, Jong-Min Kim, Jeung-Hoon Seo, Chulhyun Lee, and Chang-Hyun Oh. "Multislice B1 Mapping Method Using Magnetic Resonance Composite Spin Echo Sequences and Simultaneous Echo Refocusing." Concepts in Magnetic Resonance Part A 2023 (April 13, 2023): 1–9. http://dx.doi.org/10.1155/2023/7642095.
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Radiofrequency (RF) transmit field (B1) mapping is a promising method in mitigating the B1 inhomogeneity in various magnetic resonance imaging (MRI) applications. Although several phase- or magnitude-based B1 mapping methods have been proposed, these methods often require complex modeling, long acquisition time, or specialized MRI sequences. A recently introduced simultaneous echo refocusing (SER) technique can be applied in the B1 mapping method to extend the three-dimensional (3D) spatial coverage only without long data acquisition. Therefore, in this study, a multislice B1 mapping method using composite spin echo sequences and SER techniques is proposed to obtain more accurate B1 mapping with short data acquisition time. To evaluate the performance of the proposed B1 mapping method, computational simulations were performed and compared with Morrell’s method, double angle method, and Yarnykh’s method. These results showed that the angle-to-noise ratio of the proposed B1 mapping method has wider B1 range compared to that of other B1 mapping methods. In addition, the proposed B1 mapping methods were compared to the multislice iterative signal intensity mapping method in both phantom and in vivo human experiments, and there was no remarkable difference between the two methods regarding the flip angle distribution in these experiments. Based on these results, this study demonstrated that the proposed B1 mapping method is suitable for accurately measuring B1 propagation under the condition providing reduced scan time and wider 3D coverage of B1 mapping by applying composite RF pulse and SER techniques into the phase-sensitive method.
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Zhou, Tao, Yajun Geng, Jie Chen, Chuanliang Sun, Dagmar Haase, and Angela Lausch. "Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables." Remote Sensing 11, no. 24 (December 7, 2019): 2934. http://dx.doi.org/10.3390/rs11242934.
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Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes.
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Fathololoumi, Solmaz, Mohammad Karimi Firozjaei, and Asim Biswas. "An Innovative Fusion-Based Scenario for Improving Land Crop Mapping Accuracy." Sensors 22, no. 19 (September 30, 2022): 7428. http://dx.doi.org/10.3390/s22197428.
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The accuracy of land crop maps obtained from satellite images depends on the type of feature selection algorithm and classifier. Each of these algorithms have different efficiency in different conditions; therefore, developing a suitable strategy for combining the capabilities of different algorithms in preparing a land crop map with higher accuracy can be very useful. The objective of this study was to develop a fusion-based framework for improving land crop mapping accuracy. First, the features were retrieved using the Sentinel 1, Sentinel 2, and Landsat-8 imagery. Then, training data and various feature selection algorithms including recursive feature elimination (RFE), random forest (RF), and Boruta were used for optimal feature selection. Various classifiers, including artificial neural network (ANN), support vector machine (SVM), and RF, were implemented to create maps of land crops relying on optimal features and training data. After that, in order to increase the result accuracy, maps of land crops derived from several scenarios were fused using a fusion-based voting strategy at the level of decision, and new maps of land crops and classification uncertainty maps were prepared. Subsequently, the performance of different scenarios was evaluated and compared. Among the feature selection algorithms, RF accuracy was higher than RFE and Boruta. Moreover, the efficiency of RF was higher than SVM and ANN. The overall accuracy of the voting scenario was higher than all other scenarios. The finding of this research demonstrated that combining the features’ capabilities extracted from sensors in different spectral ranges, different feature selection algorithms, and classifiers improved the land crop classification accuracy.
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Feng, Yang, Qiong Wu, Jiajia Yang, Satoshi Takahashi, Yoshimichi Ejima, Jinglong Wu, and Ming Zhang. "Eccentricity Effect of Deformation Detection for Radial Frequency Patterns With Their Centers at Fixation Point." Perception 49, no. 8 (August 2020): 858–81. http://dx.doi.org/10.1177/0301006620936473.
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We measured the eccentricity effect of deformation thresholds of circular contours for two types of the radial frequency (RF) patterns with their centers at the fixation point: constant circular contour frequency (CCF) RF patterns and constant RF magnified (retino-cortical scaling) RF patterns. We varied the eccentricity by changing the mean radius of the RF patterns while keeping the centers of the RF patterns at the fixation point. Our peripheral stimulus presentation was distinguished from previous studies which have simply translated RF patterns at different locations in the visual field. Sensitivity for such shape discrimination fell off as the moderate and high CCF patterns were presented on more eccentric sites but did not as the low CCF patterns. However, sensitivity held constant as the magnified RF patterns were presented on more eccentric sites, indicating that the eccentricity effects observed for the high and moderate CCF patterns were neutralized by retinocortical mapping. Notably, sensitivity for the magnified RF patterns with large radii (4°–16°) presented in the peripheral field revealed a similar RF dependence observed for RF patterns with small radii (0.25°–1.0°) presented at the fovea in previous studies.
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Mazarire, Theresa Taona, Phathutshedzo Eugene Ratshiedana, Adolph Nyamugama, Elhadi Adam, and George Chirima. "Exploring machine learning algorithms for mapping crop types in a heterogeneous agriculture landscape using Sentinel-2 data. A case study of Free State Province, South Africa." South African Journal of Geomatics 9, no. 2 (September 7, 2022): 333–47. http://dx.doi.org/10.4314/sajg.v9i2.22.
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Accurate and detailed studies in crop mapping are crucial in precision agriculture, yield estimations, and crop monitoring. This study focused on exploring the utility of Sentinel-2 data in mapping of crop types and testing the two machine learning algorithms which are Random Forest and Support Vector Machine performance in classifying crop types in a heterogeneous agriculture landscape in Free state province, South Africa. Nine crop types were successfully classified. The utility and contribution of different bands for classification were evaluated using RF mean decrease GINI for variable importance. Validation of results was done using a confusion matrix which produced overall accuracy, errors and prediction measures. The best performance was attained by SVM with an overall accuracy of 95% and a kappa value of 94%. RF also performed fairly well with 85% of overall accuracy and kappa value of 83%. It was concluded that Sentinel-2 data performs better using the SVM classifier compared to RF classifier.
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Kuhn, Stephen, Matthew J. Cracknell, Anya M. Reading, and Stephanie Sykora. "Identification of intrusive lithologies in volcanic terrains in British Columbia by machine learning using random forests: The value of using a soft classifier." GEOPHYSICS 85, no. 6 (November 1, 2020): B249—B258. http://dx.doi.org/10.1190/geo2019-0461.1.
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Identifying the location of intrusions is a key component in exploration for porphyry Cu ± Mo ± Au deposits. In typical porphyry terrains, in the absence of outcrop, intrusions can be difficult to discriminate from the compositionally similar volcanic and volcanoclastic sedimentary rocks in which they are emplaced. The ability to produce lithological maps at an early exploration stage can significantly reduce costs by assisting in planning and prioritization of detailed mapping and sampling. Additionally, a data-driven strategy provides opportunity for the discovery of intrusions not identified during conventional mapping and interpretation. We used random forests (RF), a supervised machine-learning algorithm, to classify rock types throughout the Kliyul porphyry prospect in British Columbia, Canada. Rock types determined at geochemical sampling sites were used as training data. Airborne magnetic and radiometric data, geochemistry, and topographic data were used in classification. Results were validated using First Quantum Minerals’ geologic map, which includes additional detail from targeted location and transect mapping. The petrophysical and compositional similarity of rock types resulted in a noisy classification. Intrusions, particularly the more discrete, were inconsistently predicted, likely due to their limited extent relative to data sampling intervals. Closer examination of class membership probabilities (CMPs) identified locations where the probability of an intrusion being present was elevated significantly above the background. Indeed, a large proportion of mapped intrusions correspond to areas of elevated probability and, importantly, areas were highlighted as potential intrusions that were not identified in geologic mapping. The RF classification produced a reasonable lithological map, if lacking in resolution, but more significantly, great benefit comes from the insights drawn from the RF CMPs. Mapping the spatial distribution of elevated intrusion CMP, a soft classifier approach, produced a map product that can target intrusions and prioritize detailed mapping for mineral exploration.
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Stritt, Michael, Tobias Oesterlein, Stefan Pollnow, Armin Luik, Claus Schmitt, and Olaf Dössel. "Assessment of local high-density mapping for the analysis of radiofrequency ablation lesions in the left atrium." Current Directions in Biomedical Engineering 3, no. 2 (September 7, 2017): 109–12. http://dx.doi.org/10.1515/cdbme-2017-0176.
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AbstractRecent studies about the development of endocardial radiofrequency (RF) ablation lesions (ALs) tried to identify reliable electrogram (EGM) markers for assessment of lesion transmurality. Additional clinically relevant information for physicians can be provided by examining endocardial EGM parameters like signal morphology, amplitude or time points in the signal. We investigated EGM features of the pulmonary vein ostia before and after RF ablation for three point-shaped lesions. Using high-density (HD) mapping, local activation time (LAT) and voltage maps were created, which provided information about the RF ALs regarding the lesion size and showed activation time delay as well as low-voltage areas with bipolar peak-to-peak voltages smaller than 2mV. The time delay of the depolarization front comparing the activation times anterior and posterior to the RF AL was up to 51.5 ms. In a circular area with 5mm radius around an RF AL the mean peak-to-peak voltage decreased by 62-94% to about 0.12-0.44mV and the mean maximal absolute EGM derivative was reduced by 65-96 %. Comparing the results of this study with EGMs of similar clinical settings confirmed our expectations regarding the low-voltage areas caused by the ablation procedure. An improved understanding of the electrophysiological changes is of fundamental importance to provide more information for enhanced RF ablation assessment.