Decision Tree-based Classification Research Articles

Decision Tree-Based Classification Approach to Discover Factors Affecting Vitamin D Level with Machine Learning

Purpose: Vitamin D level is emphasized as an important biomarker in determining risk factors for different diseases. Vitamin D is an important vitamin for human health and its deficiency is associated with serious health problems. Therefore, it is of great importance to detect vitamin D deficiency, which can be easily prevented and treated. The possible relationship between vitamin D deficiency and musculoskeletal pain, osteoporosis, diabetes mellitus, hypertension is frequently discussed in researches. In this research, it is aimed to analyze the factors in determining the vitamin D level and the decision rules related to it. Methods: A descriptive framework based on one of the machine learning techniques, that is decision tree is followed. The data used to create the decision rules were obtained from volunteers between the ages of 18-85 who applied to Izmir Katip Çelebi University Atatürk Training and Research Hospital Infectious Diseases and Family Medicine Polyclinics and agreed to participate in the study between 01.03.2017 and 01.09.2017. Results: It was observed that age, gender and laboratory test values are strong predictors for vitamin D level. As a result of two CART (Classification and Regression Trees) models, %90.47 and %95 predictive accuracies were observed respectively. In the first model, uric acid, age and creatine; in the second model TSH, ALP and smoking(yes) were the most important three biomarkers affecting vitamin D level. Discussion: The collected features give a comprehensive list of variables that have an effect on vitamin D in the dataset under consideration. Important findings of the study include not only the identification of these variables, but also the effective categorization determination procedures. In contrast to previous research, the Age variable is the most influential factor within the scope of this dataset, which includes demographic information on patients and their existing disorders.

Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor.

To investigate the performance of structural MRI cortical and subcortical morphometric data combined with blink-reflex recovery cycle (BRrc) values using machine learning (ML) models in distinguishing between essential tremor (ET) with resting tremor (rET) and classic ET. We enrolled 47 ET, 43 rET patients and 45 healthy controls (HC). All participants underwent brain 3 T-MRI and BRrc examination at different interstimulus intervals (ISIs, 100-300 msec). MRI data (cortical thickness, volumes, surface area, roughness, mean curvature and subcortical volumes) were extracted using Freesurfer on T1-weighted images. We employed two decision tree-based ML classification algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) combining MRI data and BRrc values to differentiate between rET and ET patients. ML models based exclusively on MRI features reached acceptable performance (AUC: 0.85-0.86) in differentiating rET from ET patients and from HC. Similar performances were obtained by ML models based on BRrc data (AUC: 0.81-0.82 in rET vs. ET and AUC: 0.88-0.89 in rET vs. HC). ML models combining imaging data (cortical thickness, surface, roughness, and mean curvature) together with BRrc values showed the highest classification performance in distinguishing between rET and ET patients, reaching AUC of 0.94 ± 0.05. The improvement in classification performances when BRrc data were added to imaging features was confirmed by both ML algorithms. This study highlights the usefulness of adding a simple electrophysiological assessment such as BRrc to MRI cortical morphometric features for accurately distinguishing rET from ET patients, paving the way for a better classification of these ET syndromes.

Evidence for identification of acute myeloid leukemia using peripheral blood's infrared spectrum and logistic regression

Invasive damage, poor prognosis and survival are problems for the traditional diagnostic and therapeutic methods of acute myeloid leukemia (AML), especially for the middle-aged and elderly. High sensitivity, less pain and fast speed are demands of AML diagnosis or long-term regular monitoring. Infrared (IR) spectroscopy has the potential to compensate for existing deficiencies and is explored in our work. Spectral differences between AML patients and controls are observed on the IR spectra, which indicate the changes in lipids and proteins in patients. The peak shift at 1390 cm−1 occurs on both peripheral blood (PB) and bone marrow (BM) spectra of AML patients making it possible to use PB rather than BM as the diagnostic source, which can relieve the suffering of diagnosis and monitoring. The logistic regression-based classification model can perfectly identify AML patients and controls with an accuracy of 100 % and the area under curve is equal to 1.00. Besides, some markers for identifying AML patients and controls are found. Statistical analysis shows that the peak area ratio A1451/A1390 has a significant difference, and the decision tree-based classification model finds that on the first derivative spectrum, the position of the largest difference is at 1391 cm−1. Furthermore, the proposed criterion based on markers we found can distinguish AML patients and controls with high accuracy of 95 %. Results show that PB’s IR spectrum combined with classification models can monitor the molecular-level biochemical changes and accurately identify AML patients and controls, which is helpful for the effective and efficient diagnosis and prognosis of AML.

Multi-Stage Learning Framework Using Convolutional Neural Network and Decision Tree-Based Classification for Detection of DDoS Pandemic Attacks in SDN-Based SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems, which play a critical role in monitoring, managing, and controlling industrial processes, face flexibility, scalability, and management difficulties arising from traditional network structures. Software-defined networking (SDN) offers a new opportunity to overcome the challenges traditional SCADA networks face, based on the concept of separating the control and data plane. Although integrating the SDN architecture into SCADA systems offers many advantages, it cannot address security concerns against cyber-attacks such as a distributed denial of service (DDoS). The fact that SDN has centralized management and programmability features causes attackers to carry out attacks that specifically target the SDN controller and data plane. If DDoS attacks against the SDN-based SCADA network are not detected and precautions are not taken, they can cause chaos and have terrible consequences. By detecting a possible DDoS attack at an early stage, security measures that can reduce the impact of the attack can be taken immediately, and the likelihood of being a direct victim of the attack decreases. This study proposes a multi-stage learning model using a 1-dimensional convolutional neural network (1D-CNN) and decision tree-based classification to detect DDoS attacks in SDN-based SCADA systems effectively. A new dataset containing various attack scenarios on a specific experimental network topology was created to be used in the training and testing phases of this model. According to the experimental results of this study, the proposed model achieved a 97.8% accuracy rate in DDoS-attack detection. The proposed multi-stage learning model shows that high-performance results can be achieved in detecting DDoS attacks against SDN-based SCADA systems.

Improving Student Performance Prediction Through Feature Selection: Insights from ‘Offee’ Assessment Data during the Covid-19 Pandemic

Post-pandemic student performance prediction has gained considerable attention within the academic community. Educational institutions worldwide have adopted blended learning approaches for delivering education and assessments. In this study, we harnessed data from the ‘Offee’ assessment platform based in Mumbai, India, spanning from September 2020 to October 2022. This dataset allows us to analyze the real impact of the Covid-19 pandemic on examinations.To tackle the challenges posed by high-dimensional data, we employed feature selection techniques to retain only the most significant features for predicting student performance. Specifically, we assessed the effectiveness of two decision tree-based methods, J48 classifier and Random Forest, in conjunction with the ranker search method using wrapper subset evaluation. The resulting feature set was then utilized to train and test a Support Vector Machine model for predicting student performance.Our findings reveal that both J48 and Random Forest feature selection methods yield impressive precision accuracy rates for student performance prediction, achieving 93.14% and 86.24%, respectively. In contrast, using the dataset without feature selection resulted in lower precision accuracy, standing at 79.9%, and consumed more time during model training and development.In summary, this study underscores the significance of feature selection in enhancing the accuracy of student performance prediction compared to utilizing the dataset without such feature curation.

Towards Automated Vocal Mode Classification in Healthy Singing Voice—An XGBoost Decision Tree-Based Machine Learning Classifier

Towards Automated Vocal Mode Classification in Healthy Singing Voice—An XGBoost Decision Tree-Based Machine Learning Classifier

On the integration of decision trees with mixture cure model.

The mixture cure model is widely used to analyze survival data in the presence of a cured subgroup. Standard logistic regression-based approaches to model the incidence may lead to poor predictive accuracy of cure, specifically when the covariate effect is non-linear. Supervised machine learning techniques can be used as a better classifier than the logistic regression due to their ability to capture non-linear patterns in the data. However, the problem of interpret-ability hangs in the balance due to the trade-off between interpret-ability and predictive accuracy. We propose a new mixture cure model where the incidence part is modeled using a decision tree-based classifier and the proportional hazards structure for the latency part is preserved. The proposed model is very easy to interpret, closely mimics the human decision-making process, and provides flexibility to gauge both linear and non-linear covariate effects. For the estimation of model parameters, we develop an expectation maximization algorithm. A detailed simulation study shows that the proposed model outperforms the logistic regression-based and spline regression-based mixture cure models, both in terms of model fitting and evaluating predictive accuracy. An illustrative example with data from a leukemia study is presented to further support our conclusion.

Machine learning-based soft-sensor development for road quality classification

Vibrations in road vehicles cause several harmful effects, health problems can occur for the passengers, and mechanical damage can occur to the vehicle components. Given the health, safety, and financial issues that arise, keeping the road network in good condition and detecting road defects as early as possible requires an extensive monitoring system. Related to this, our study presents the development of hardware and software for a low-cost, multi-sensor road quality monitoring system for passenger vehicles. The developed monitoring system can classify road sections according to their quality parameters into four classes. In order to detect vibrations in the vehicle, accelerometers and gyroscope sensors are installed at several points. Then, a machine learning-based soft-sensor development is introduced. Besides noise filtering, each data point is resampled by spatial frequency to reduce the velocity dependence. Subsequently, a decision tree-based classification model is trained using features from the power spectrum and principal component analysis. The classification algorithm is validated and tested with measurement data in a real-world environment. In addition to reviewing the accuracy of the model, we examine the correlation of the data measured in the cabin and on the suspension to see how much additional information is provided by the sensor on the axle.

Automated classification ofChandraX-ray point sources using machine learning methods

ABSTRACTA large number of unidentified sources found by astronomical surveys and other observations necessitate the use of an automated classification technique based on machine learning (ML) methods. The aim of this paper is to find a suitable automated classifier to identify the point X-ray sources in the Chandra Source Catalogue (CSC) 2.0 in the categories of active galactic nuclei (AGN), X-ray emitting stars, young stellar objects (YSOs), high-mass X-ray binaries (HMXBs), low-mass X-ray binaries (LMXBs), ultra luminous X-ray sources (ULXs), cataclysmic variables (CVs), and pulsars. The catalogue consists of ≈317 000 sources, out of which we select 277 069 point sources based on the quality flags available in CSC 2.0. In order to identify unknown sources of CSC 2.0, we use MW features, such as magnitudes in optical/ultraviolet bands from Gaia-EDR3, Sloan Digital Sky Survey, and GALEX, and magnitudes in infrared bands from 2MASS, WISE, and MIPS-Spitzer, in addition to X-ray features (flux and variability) from CSC 2.0. We find the Light Gradient Boosted Machine, an advanced decision tree-based ML classification algorithm, suitable for our purpose and achieve 93 per cent precision, 93 per cent recall score, and 0.91 Mathew’s Correlation coefficient score. With the trained classifier, we identified 54 770 (14 066) sources with more than 3σ (4σ) confidence, out of which there are 32 600 (8574) AGNs, 16,148 (5,166) stars, 5,184 (208) YSOs, 439 (46) HMXBs, 197 (71) LMXBs, 50 (0) ULXs, 89 (1) CVs, and 63 (0) pulsars. This method can also be useful for identifying sources of other catalogues reliably.

Effective hybrid feature subset selection for multilevel datasets using decision tree classifiers

Feature selection is one of the most significant procedures in machine learning algorithms. It is particularly to improve the performance and prediction accuracy for complex data classification. This paper discusses a hybrid feature selection technique with the decision tree-based classification algorithm. The feature selected using information gain (IG) is combined with the features selected from ReliefF which generates the resultant feature subset. Then the resultant feature subset is in turn combined with a correlation-based feature selection (CFS) method to generate the aggregated feature subset. To perform classification accuracy on the aggregated feature subset, different decision trees-based classification algorithm such as C4.5, decision stumps, naive Bayes tree, and random forest with ten-fold cross-validation have been deployed. To check the prediction accuracy of the proposed work eight different multilevel University of California, Irvine (UCI) machine learning datasets have been used with minimum to maximum numbers of features. The main objective of the hybrid feature selection is to improve the classification accuracy, prediction and to reduce the execution time using standard datasets.

Effectiveness of frequency domain parameters in decision tree-based classification of GMAW defects

Effectiveness of frequency domain parameters in decision tree-based classification of GMAW defects

Malware Detection Using Decision Tree Algorithm Based on Memory Features Engineering

Malware is malicious software that can harm, manipulate, steal from victim's device system. Due to the diverse needs of using internet services, security threats are also increasingly difficult to detect. now attackers are starting to develop malware that can change their own signature which is referred to as polymorphism. Therefore, improvements in the traditional approach to detecting the presence of malware are needed to be improved. One of the malware detection approaches, memory-based analysis technique has proven to be a powerful and effective analytical technique in studying malware behavior. In this study, the implementation of a Decision Tree-based classification algorithm was carried out to analyze the data set. Classifier model was created for the purpose of classifying malware based on memory features engineering. The result shows that the Decision Tree machine learning algorithm has been well performed with accuracy to 99.982 %, a false positive rate equal to 0.1% and precision equal to 99.977%

Secure Data In Cloud Using Machine Learning Based On Intrusion Detection System

Huge amounts of network traffic are produced daily due to the introduction of new technologies like cloud computing and big data, and the intrusion detection system must dynamically gather and evaluate the data produced by the incoming traffic. However, not all features in a huge dataset help describe the traffic, so limiting and choosing only a few suitable features may increase the intrusion detection system's speed and accuracy. Based on the detection rate each feature has established throughout the selection process, a feature selection method has been developed in this study to remove irrelevant features and identify the features that would help enhance the detection rate. Recursive feature reduction was used in association with a decision tree-based classifier to accomplish that goal, and the appropriate relevant features were later found. This methodology was utilized in this research, along with scikit-learn, a machine learning toolkit built in Python, to analyze the NSL-KDD dataset, an upgraded version of the KDD 1999 dataset. This method allowed for the identification of appropriate features within the dataset, improving accuracy rates. Machine learning uses the C4.5 classification decision tree method, which is based on the ID3 algorithm. The assumption that feature selection considerably enhances classifier performance is supported by these findings.

A simple decision tree-based disturbance monitoring system for VSC-based HVDC transmission link integrating a DFIG wind farm

Fault detection and classification is a key challenge for the protection of High Voltage DC (HVDC) transmission lines. In this paper, the Teager–Kaiser Energy Operator (TKEO) algorithm associated with a decision tree-based fault classifier is proposed to detect and classify various DC faults. The Change Identification Filter is applied to the average and differential current components, to detect the first instant of fault occurrence (above threshold) and register a Change Identified Point (CIP). Further, if a CIP is registered for a positive or negative line, only three samples of currents (i.e., CIP and each side of CIP) are sent to the proposed TKEO algorithm, which produces their respective 8 indices through which the, fault can be detected along with its classification. The new approach enables quicker detection allowing utility grids to be restored as soon as possible. This novel approach also reduces computing complexity and the time required to identify faults with classification. The importance and accuracy of the proposed scheme are also thoroughly tested and compared with other methods for various faults on HVDC transmission lines.

PD53-07 DETECTION OF EARLY-STAGE BLADDER CANCER USING PLASMA EXTRACELLULAR VESICLE PROTEIN BIOMARKERS

You have accessJournal of UrologyCME1 May 2022PD53-07 DETECTION OF EARLY-STAGE BLADDER CANCER USING PLASMA EXTRACELLULAR VESICLE PROTEIN BIOMARKERS Juan Hinestrosa, Jean Lewis, Gregor Schroder, Heath Balcer, and Rajaram Krishnan Juan HinestrosaJuan Hinestrosa More articles by this author , Jean LewisJean Lewis More articles by this author , Gregor SchroderGregor Schroder More articles by this author , Heath BalcerHeath Balcer More articles by this author , and Rajaram KrishnanRajaram Krishnan More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002630.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: There is a need for earlier bladder cancer screening to reduce lifetime costs and improve patient outcomes. Urinary protein biomarkers have shown some promise, but reported sensitivities appear compromised by low and/or variable biomarker concentrations. In this study, we investigated the potential of using extracellular vesicles (EV) circulating in the blood to detect stage I and II bladder cancer. Using an alternating current electrokinetic (ACE) platform to purify EVs from plasma, we show how a multi-marker EV protein-based methodology can be used to discriminate bladder cancer cases from controls in stage I and II cases. METHODS: Plasma samples were obtained from a biorepository. We used an ACE microelectrode array to isolate plasma EVs of stage I and II bladder cancer cases (N=58) or controls (N=387). EV proteins were quantified using multiplex protein immunoassays for 54 cancer-related proteins. EV protein expression patterns were analyzed using a boosted decision tree-based binary classification algorithm. To identify biomarker subsets that optimized sensitivity of the model, the permutation importance at sensitivity >99% was calculated for each feature. To evaluate biomarker and algorithm performance, a round of 10 repetitions of 5-fold cross validation was done using both the optimized hyper-parameters and the features selected via the permutation importance. RESULTS: We found that relative concentrations of a set of protein biomarkers carried by circulating plasma EVs was sufficient to discriminate bladder cancer cases from controls, with enough sensitivity to detect stage I and II cancers. The EV protein-based classifier had an overall area under curve (AUC) of 0.964 (95% CI: 0.947–0.981) with a sensitivity of 56.9% (95% CI: 44.1%–68.8%) at 99.2% specificity (95% CI: 97.8%-99.7%). At a lower 95.6% specificity (95% CI: 93.1%-97.2%) the sensitivity was 72.4% (95% CI: 59.8%-82.3%). CONCLUSIONS: Testing of EV-associated plasma proteins may enable early-stage I and II bladder cancer detection where treatment is simpler and does not require bladder resection. The classifier’s performance in this pilot, case-control study showed encouraging results for the initial 54 biomarkers studied. Since we have observed much higher specificities for other early-stage cancer types, the performance for bladder cancer will likely be improved by inclusion of additional biomarker proteins. Future efforts will include examining additional cancer types and evaluating classifier performance using samples from donors with related benign conditions with the aim of a pan-cancer early detection assay. Source of Funding: Biological Dynamics © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e912 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Juan Hinestrosa More articles by this author Jean Lewis More articles by this author Gregor Schroder More articles by this author Heath Balcer More articles by this author Rajaram Krishnan More articles by this author Expand All Advertisement PDF DownloadLoading ...

Change detection filter technique-based fault analysis of HVDC transmission line

PurposeThis research paper aims to investigate the change detection filter technique with a decision tree-based event (fault type) classifier for recognizing and categorizing power system disturbances on the high-voltage DC (HVDC) transmission link.Design/methodology/approachA change detection filter is used to the average and differential current components, which detects the point of fault initiation and records a change detection point (CDP). The half-cycle differential and average currents on both sides of the CDP are sent through the signal processing unit, which produces the respective target. The extracted target indices are sent through a decision tree-based fault classifier mechanism for fault classification.FindingsIn comparison with conventional differential current protection systems, the developed framework is faster in fault detection and classification and provides great accuracy. The new technology allows for prompt identification of the fault category, allowing electrical grids to be restored as quickly as possible to minimize economic losses. This novel technology enhances efficiency in terms of reducing computing complexity.Research limitations/implicationsSetting a threshold value for identification is one of the limitations. To bring the designed system into stability condition before creating faults on it is another limitation. Reducing the computational burden is one of the limitations.Practical implicationsCreating a practical system in laboratory is difficult as it is a HVDC transmission line. Apart from that, installing rectifier and converter section for HVDC transmission line is difficult in a laboratory setting.Originality/valueThe suggested scheme’s importance and accuracy have been rigorously validated for the standard HVDC transmission system, subjected to various types of DC fault, and the results show the proposed algorithm would be a feasible alternative to real-time applications.

Decision tree-based classification as a support to diagnosis in the Alzheimer's disease continuum using cerebrospinal fluid biomarkers: insights from automated analysis.

Objective:Cerebrospinal fluid (CSF) biomarkers add accuracy to the diagnostic workup of cognitive impairment by illustrating Alzheimer’s disease (AD) pathology. However, there are no universally accepted cutoff values for the interpretation of AD biomarkers. The aim of this study is to determine the viability of a decision-tree method to analyse CSF biomarkers of AD as a support for clinical diagnosis.Methods:A decision-tree method (automated classification analysis) was applied to concentrations of AD biomarkers in CSF as a support for clinical diagnosis in older adults with or without cognitive impairment in a Brazilian cohort. In brief, 272 older adults (68 with AD, 122 with mild cognitive impairment [MCI], and 82 healthy controls) were assessed for CSF concentrations of Aβ1-42, total-tau, and phosphorylated-tau using multiplexed Luminex assays; biomarker values were used to generate decision-tree algorithms (classification and regression tree) in the R statistical software environment.Results:The best decision tree model had an accuracy of 74.65% to differentiate the three groups. Cluster analysis supported the combination of CSF biomarkers to differentiate AD and MCI vs. controls, suggesting the best cutoff values for each clinical condition.Conclusion:Automated analyses of AD biomarkers provide valuable information to support the clinical diagnosis of MCI and AD in research settings.

Patient-Reported Outcome+ Platform for Remote Symptom Management, Featuring Automated Triage System

<h3>Purpose/Objective(s)</h3> Despite the promising results, symptom monitoring is not widely implemented in the clinic. This is largely due to the additional staffing that is needed to triage the increased patients' symptom reporting. We herein develop a PRO+ platform for remote symptom management, where the plus sign stands for automated triage pathways. This includes AI and evidence-based triage chatbot and severity classification algorithm, to reduce manual triage needs. <h3>Materials/Methods</h3> 22 most common symptoms were included in the triage system. For each symptom, triage pathways were built in chatbot format. Typically, a triage starts with onset questions, like "when did it start?", "how many episodes?", followed by PRO CTCAE evaluation, how daily life was affected, home medication and remedies patients used, associated symptoms, etc. For most questions, multiple choices were identified as potential answers for patients to choose from. I.e., 10 scales were provided for pain evaluation. Some questions offer sharing pictures or free text. A decision tree-based classification algorithm was developed to classify the reported symptoms to non-urgent, urgent, and emergent situations. Three metrics were used for ongoing evaluation during development. 1) Patient engagement rate. Volunteer patients used the system to report symptoms. If a chatbot conversation was initiated and finished, it was counted as a successful engagement. 2) Chatbot triage efficiency. Nurses reviewed the reported symptom details and marked the situations that enough information was collected to provide patients clinical advice. Corresponding percentage was calculated. Although we expect that there will always be cases that additional communication is needed to triage patients, we want to collect enough information to triage most straightforward cases. 3) Classification accuracy. The classification algorithm results were compared with manual results by nurses, and discrepancy was reported. <h3>Results</h3> The PRO+ platform was built. Patients can select any symptom to report. Once a symptom is selected, the chatbot will carry a conversation with patient to collect necessary information for triage. The chat bot conversation is adapted real-time to the reported symptoms and conversation. 150 volunteer patients reported symptoms, and the engagement rate was 91%. 80% of the time the chatbot had collected enough information for nurses to provide clinical advice. The severity classification discrepancy was 95%. <h3>Conclusion</h3> A PRO+ platform was built for symptom management. It has automated triage pathways that can potentially allow clinics to provide remote symptom monitoring with reduced manual triage needs. More study is needed to examine the chatbot questions, severity classification performance, and staffing model required before wide adoption in the clinic.

Decision tree-based classification in coastal area integrating polarimetric SAR and optical data

PurposeAs one of the world's most productive ecosystems, ecological land plays an important role in regional and global environments. Utilizing advanced optical and synthetic aperture radar (SAR) data for land cover/land use research becomes increasingly popular. This research aims to investigate the complementarity of fully polarimetric SAR and optical imaging for ecological land classification in the eastern coastal area of China.Design/methodology/approachFour polarimetric decomposition methods, namely, H/Alpha, Yamaguchi3, VanZyl3 and Krogager, were applied to Advanced Land Observing Satellite (ALOS) SAR image for scattering parameter extraction. These parameters were merged with ALOS optical parameters for subsequent classification using the object-based quick, unbiased, efficient statistical tree decision tree method.FindingsThe experimental results indicate that an improved classification performance was obtained in the decision level when merging the two data sources. In fact, unlike classification using only optical images, the proposed approach allowed to distinguish ecological land with similar spectrum but different scattering. Moreover, unlike classification using only polarimetric information, the integration of polarimetric and optical data allows to accurately distinguish reed from artemisia and sand from salt field and therefore achieve a detailed classification of the coastal area characteristics.Originality/valueThis research proposed an integrated classification method for coastal ecological land with polarimetric SAR and optical data. The object-based and decision-level fusion enables effective ecological land classification in coastal area was verified.

Automated classification of heat sources detected using SWIR remote sensing

The potential of shortwave infrared (SWIR) remote sensing to detect hotspots has been investigated using satellite data for decades. The hotspots detected by satellite SWIR sensors include very high-temperature heat sources such as wildfires, volcanoes, industrial activity, or open burning. This study proposes an automated classification method of heat source detected utilizing Landsat 8 and Sentinel-2 data. We created training data of heat sources via visual inspection of hotspots detected by Landsat 8. A scheme to classify heat sources for daytime data was developed by combining classification methods based on a Convolutional Neural Network (CNN) algorithm utilizing spatial features and a decision tree algorithm based on thematic land-cover information and our time series detection record. Validation work using 10,959 classification results corresponding to hotspots acquired from May 2017 to July 2019 indicated that the two classification results were in 79.7% agreement. For hotspots where the two classification schemes agreed, the classification was 97.9% accurate. Even when the results of the two classification schemes conflicted, either was correct in 73% of the samples. To improve the accuracy, the heat source category was re-allocated to the most probable category corresponding to the combination of the results from the two methods. Integrating the two approaches achieved an overall accuracy of 92.8%. In contrast, the overall accuracy for heat source classification during nighttime reached 79.3% because only the decision tree-based classification was applicable to limited available data. Comparison with the Visible Infrared Imaging Radiometer Suite (VIIRS) fire product revealed that, despite the limited data acquisition frequency of Landsat 8, regional tendencies in hotspot occurrence were qualitatively appropriate for an annual period on a global scale.