scholarly journals Comparison of Machine Learning Methods Applied to SAR Images for Forest Classification in Mediterranean Areas

2020 ◽  
Vol 12 (3) ◽  
pp. 369 ◽  
Author(s):  
Alessandro Lapini ◽  
Simone Pettinato ◽  
Emanuele Santi ◽  
Simonetta Paloscia ◽  
Giacomo Fontanelli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Central Italy ◽  
Alos Palsar ◽  
Sar Images ◽  
Random Forest Classification ◽  
Envisat Asar ◽  
Learning Framework ◽  
Forest Classification ◽  
Average Accuracy ◽  
Reference Map

In this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo-SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in-situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L-, C- and X-bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non-forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient ( σ ¯ °) was computed for each sensor-polarization pair and labeled on a pixel basis according to the reference map. Several classification methods based on the machine learning framework were applied and validated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers’ performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L- and X-bands. In the former case, the best overall average accuracy (83.1%) is achieved by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors.

scholarly journals INTEGRATED SHORELINE EXTRACTION APPROACH WITH USE OF RASAT MS AND SENTINEL-1A SAR IMAGES

2017 ◽  
Vol IV-2/W4 ◽  
pp. 445-449 ◽  
Author(s):  
N. Demir ◽  
S. Oy ◽  
F. Erdem ◽  
D. Z. Şeker ◽  
B. Bayram
Keyword(s):  
Water Body ◽  
Accuracy Assessment ◽  
Extraction Methods ◽  
Training Data ◽  
Sar Images ◽  
Data Set ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Body Segmentation ◽  
Land Water

Shorelines are complex ecosystems and highly important socio-economic environments. They may change rapidly due to both natural and human-induced effects. Determination of movements along the shoreline and monitoring of the changes are essential for coastline management, modeling of sediment transportation and decision support systems. Remote sensing provides an opportunity to obtain rapid, up-to-date and reliable information for monitoring of shoreline. In this study, approximately 120 km of Antalya-Kemer shoreline which is under the threat of erosion, deposition, increasing of inhabitants and urbanization and touristic hotels, has been selected as the study area. In the study, RASAT pansharpened and SENTINEL-1A SAR images have been used to implement proposed shoreline extraction methods. The main motivation of this study is to combine the land/water body segmentation results of both RASAT MS and SENTINEL-1A SAR images to improve the quality of the results. The initial land/water body segmentation has been obtained using RASAT image by means of Random Forest classification method. This result has been used as training data set to define fuzzy parameters for shoreline extraction from SENTINEL-1A SAR image. Obtained results have been compared with the manually digitized shoreline. The accuracy assessment has been performed by calculating perpendicular distances between reference data and extracted shoreline by proposed method. As a result, the mean difference has been calculated around 1 pixel.

scholarly journals Analysis Of The Effect Of Knowledge On Entrepreneurship Readiness Using Random Forest Classification Machine Learning

2021 ◽  
Vol 23 ◽  
pp. 134-149
Author(s):  
Ariyono Setiawan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Random Forest ◽  
Rapid Development ◽  
Air Transportation ◽  
Sample Population ◽  
Random Forest Classification ◽  
Main Requirement ◽  
Forest Classification ◽  
Entrepreneurial Readiness

Entrepreneurship is a phenomenon that has an important influence on the progress and welfare of the world, so that entrepreneurship is used as the base of economic development. Psychologically, entrepreneurs are people who have a strong internal drive as an effort to achieve certain goals so that they have a tendency to experiment in showing a character that is free from the control of others. Entrepreneurship can be seen from various points of view. The angle and context in question are views from several fields, namely according to economists, management, business people, psychologists and investors. The main requirement that an entrepreneur must have is entrepreneurial knowledge. entrepreneurial readiness is determined by the knowledge possessed and experience in conducting a business (Kurniawati, 2019). In the midst of the rapid development of artificial intelligence (AI) technology today. Not many people know that artificial intelligence consists of several branches, one of which is machine learning. This machine learning (ML) technology is one of the branches of AI that is very interesting. The sample population in this study was obtained from the air transportation school consisting of 7 populations. Data analysis is done by using . The research location is an air transportation school with Machine Learning Random Forest Classification with a population of cadets, lecturers and the general public

scholarly journals Machine learning classification of significant tornadoes and hail in the U.S. using ERA5 proximity soundings

2021 ◽  
Author(s):  
Vittorio A. Gensini ◽  
Cody Converse ◽  
Walker S. Ashley ◽  
Mateusz Taszarek
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Random Forest ◽  
Characteristic Curve ◽  
Grid Point ◽  
Severe Weather ◽  
Theoretic Approach ◽  
Random Forest Classification ◽  
Machine Learning Classification ◽  
Forest Classification

AbstractPrevious studies have identified environmental characteristics that skillfully discriminate between severe and significant-severe weather events, but they have largely been limited by sample size and/or population of predictor variables. Given the heightened societal impacts of significant-severe weather, this topic was revisited using over 150 000 ERA5 reanalysis-derived vertical profiles extracted at the grid-point nearest—and just prior to—tornado and hail reports during the period 1996–2019. Profiles were quality-controlled and used to calculate 84 variables. Several machine learning classification algorithms were trained, tested, and cross-validated on these data to assess skill in predicting severe or significant-severe reports for tornadoes and hail. Random forest classification outperformed all tested methods as measured by cross-validated critical success index scores and area under the receiver operating characteristic curve values. In addition, random forest classification was found to be more reliable than other methods and exhibited negligible frequency bias. The top three most important random forest classification variables for tornadoes were wind speed at 500 hPa, wind speed at 850 hPa, and 0–500-m storm-relative helicity. For hail, storm-relative helicity in the 3–6 km and -10 to -30 °C layers, along with 0–6-km bulk wind shear, were found to be most important. A game theoretic approach was used to help explain the output of the random forest classifiers and establish critical feature thresholds for operational nowcasting and forecasting. A use case of spatial applicability of the random forest model is also presented, demonstrating the potential utility for operational forecasting. Overall, this research supports a growing number of weather and climate studies finding admirable skill in random forest classification applications.

scholarly journals Humboldtian Diagnosis of Peach Tree (Prunus persica) Nutrition Using Machine-Learning and Compositional Methods

Agronomy ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 900 ◽  
Author(s):  
Debora Leitzke Betemps ◽  
Betania Vahl de Paula ◽  
Serge-Étienne Parent ◽  
Simone P. Galarça ◽  
Newton A. Mayer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Euclidean Distance ◽  
Prunus Persica ◽  
Nutrient Status ◽  
Classification Model ◽  
Data Sets ◽  
Local Conditions ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Euclidean Distances

Regional nutrient ranges are commonly used to diagnose plant nutrient status. In contrast, local diagnosis confronts unhealthy to healthy compositional entities in comparable surroundings. Robust local diagnosis requires well-documented data sets processed by machine learning and compositional methods. Our objective was to customize nutrient diagnosis of peach (Prunus persica) trees at local scale. We collected 472 observations from commercial orchards and fertilizer trials across eleven cultivars of Prunus persica and six rootstocks in the state of Rio Grande do Sul (RS), Brazil. The random forest classification model returned an area under curve exceeding 0.80 and classification accuracy of 80% about yield cutoff of 16 Mg ha−1. Centered log ratios (clr) of foliar defective compositions have appropriate geometry to compute Euclidean distances from closest successful compositions in “enchanting islands”. Successful specimens closest to defective specimens as shown by Euclidean distance allowed reaching trustful fruit yields using site-specific corrective measures. Comparing tissue composition of low-yielding orchards to that of the closest successful neighbors in two major Brazilian peach-producing regions, regional diagnosis differed from local diagnosis, indicating that regional standards may fail to fit local conditions. Local diagnosis requires well-documented Humboldtian data sets that can be acquired through ethical collaboration between researchers and stakeholders.

scholarly journals Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy

2021 ◽  
Vol 13 (4) ◽  
pp. 809
Author(s):  
Emanuele Santi ◽  
Marta Chiesi ◽  
Giacomo Fontanelli ◽  
Alessandro Lapini ◽  
Simonetta Paloscia ◽  
...  
Keyword(s):  
Preliminary Investigation ◽  
Central Italy ◽  
Forest Biomass ◽  
Ground Truth ◽  
National Forest Inventory ◽  
Mediterranean Forests ◽  
Alos Palsar ◽  
Laser Altimeter ◽  
Envisat Asar ◽  
L Band

In this paper, multi-frequency synthetic aperture radar (SAR) data at L- and C-bands (ALOS PALSAR and Envisat/ASAR) were used to estimate forest biomass in Tuscany, in Central Italy. The ground measurements of woody volume (WV, in m3/ha), which can be considered as a proxy of forest biomass, were retrieved from the Italian National Forest Inventory (NFI). After a preliminary investigation to assess the sensitivity of backscatter at C- and L-bands to forest biomass, an approach based on an artificial neural network (ANN) was implemented. The ANN was trained using the backscattering coefficient at L-band (ALOS PALSAR, HH and HV polarization) and C-band (Envisat ASAR in HH polarization) as inputs. Spatially distributed WV values for the entire test area were derived by the integration (fusion) of a canopy height map derived from the Ice, Cloud, and Land Elevation Geoscience Laser Altimeter System (ICESat GLAS) and the NFI data, in order to build a significant ground truth dataset for the training stage. The analysis of the backscattering sensitivity to WV showed a moderate correlation at L-band and was almost negligible at C-band. Despite this, the ANN algorithm was able to exploit the synergy of SAR frequencies and polarizations, estimating WV with average Pearson’s correlation coefficient (R) = 0.96 and root mean square error (RMSE) ≃ 39 m3/ha when applied to the test dataset and average R = 0.86 and RMSE ≃ 75 m3/ha when validated on the direct measurements from the NFI. Considering the heterogeneity of the scenario (Mediterranean mixed forests in hilly landscape) and the small amount of available ground measurements with respect to the spatial variability of different plots, the obtained results can be considered satisfactory. Moreover, the successful use of WV from global maps for implementing the algorithm suggests the possibility to apply the algorithm to wider areas or even to global scales.

scholarly journals A Pinnacle Technique for Detection of COVID-19 Fake News in Social Media

2020 ◽  
Vol 10 (1) ◽  
pp. 256-261
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Random Forest ◽  
General Public ◽  
Classification Model ◽  
Fake News ◽  
Random Forest Classification ◽  
Forest Classification ◽  
The World ◽  
The Impact

Today the world is gripped with fear of the most infectious disease which was caused by a newly discovered virus namely corona and thus termed as COVID-19. This is a large group of viruses which severely affects humans. The world bears testimony to its contagious nature and rapidity of spreading the illness. 50l people got infected and 30l people died due to this pandemic all around the world. This made a wide impact for people to fear the epidemic around them. The death rate of male is more compared to female. This Pandemic news has caught the attention of the world and gained its momentum in almost all the media platforms. There was an array of creating and spreading of true as well as fake news about COVID-19 in the social media, which has become popular and a major concern to the general public who access it. Spreading such hot news in social media has become a new trend in acquiring familiarity and fan base. At the time it is undeniable that spreading of such fake news in and around creates lots of confusion and fear to the public. To stop all such rumors detection of fake news has become utmost important. To effectively detect the fake news in social media the emerging machine learning classification algorithms can be an appropriate method to frame the model. In the context of the COVID-19 pandemic, we investigated and implemented by collecting the training data and trained a machine learning model by using various machine learning algorithms to automatically detect the fake news about the Corona Virus. The machine learning algorithm used in this investigation is Naïve Bayes classifier and Random forest classification algorithm for the best results. A separate model for each classifier is created after the data preparation and feature extraction Techniques. The results obtained are compared and examined accurately to evaluate the accurate model. Our experiments on a benchmark dataset with random forest classification model showed a promising results with an overall accuracy of 94.06%. This experimental evaluation will prevent the general public to keep themselves out of their fear and to know and understand the impact of fast-spreading as well as misleading fake news.

scholarly journals Performance Analysis of Different Machine Learning Algorithms for Identifying and Classifying the Failures of Traction Motors

2021 ◽  
Vol 2095 (1) ◽  
pp. 012058
Author(s):  
Xiaoyu Xian ◽  
Haichuan Tang ◽  
Yin Tian ◽  
Qi Liu ◽  
Yuming Fan
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Random Forest Classification ◽  
Machine Learning Classification ◽  
Fault Type ◽  
Traction Motors ◽  
Forest Classification

Abstract This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support vector machine and neural network classification. Through experiment, we compared these methods over 4 classification regimes and finally summarize the most suitable machine learning algorithms for different aspects of health diagnosis in traction motors area.

scholarly journals Predicting Crop yield and Effective use of Fertilizers using Machine Learning Techniques

2020 ◽  
Vol 9 (7) ◽  
pp. 1288-1292
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Crop Yield ◽  
Soil Type ◽  
Ph Value ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Random Forest Classification ◽  
Crop Selection ◽  
Forest Classification

In-season crop yield estimation has various applications such as the farmer taking corrective measures to increase the yield. We are exploring the efficient use of fertilizers. The various data mining techniques are used on data for environment. The data is related to humidity, PH. value, water, soil type and atmospheric pressure these are responsible for crop yield. This result is obtained by this algorithm are useful for farmers to take decisions about further implantation of crop yield. Crop selection method is widely used to build decision tree to overcome many problems in real time real world. One of the most important fields is decision tree. By analyzing the soil and atmosphere at particular region best crop in order to have more crop yield and the net crop yield can be predicted. This prediction will help the farmers to choose appropriate crops for their farm according to the soil type, temperature, humidity, water level, spacing depth, soil PH, season, fertilizer and months. This prediction can be carried out using Random Forest classification machine learning algorithm.

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