Search Results (14)

Comprehensive vegetation surveys are crucial for species selection and layout during the restoration of degraded island ecosystems. However, due to the poor accessibility of uninhabited islands, traditional quadrat surveys are time-consuming and labor-intensive, and it is challenging to fully identify the specific species and their spatial distributions. With miniaturized sensors and strong accessibility, high spatial and temporal resolution, Unmanned Aerial Vehicles (UAVs) have been extensively implemented for vegetation surveys. By collecting UAVs multispectral images and conducting field quadrat surveys on Anyu Island, we employ four machine learning models, namely Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Random Forest (RF) and Multiple Classifier Systems (MCS). We aim to identify the dominant species and analyze their spatial distributions according to spectral characteristics, vegetation index, topographic factors, texture features, and canopy heights. The results indicate that SVM model achieves the highest (88.55%) overall accuracy (OA) (kappa coefficient = 0.87), while MCS model does not significantly improve it as expected. Acacia confusa has the highest OA among 7 dominant species, reaching 97.67%. Besides the spectral characteristics, the inclusion of topographic factors and texture features in the SVM model can significantly improve the OA of dominant species. By contrast, the vegetation index, particularly the canopy height even reduces it. The dominant species exhibit significant zonal distributions with distance from the coastline on the Anyu Island (p < 0.001). Our study provides an effective and universal path to identify and map the dominant species and is helpful to manage and restore the degraded vegetation on uninhabited islands. Full article

Multiple sclerosis (MS) is one of the chronic and neurodegenerative diseases of the central nervous system (CNS). It generally affects motor, sensory, cerebellar, cognitive, and language functions. It is thought that identifying MS speech disorders using quantitative methods will make a significant contribution to physicians in the diagnosis and follow-up of MS patients. In this study, it was aimed to investigate the speech disorders of MS via objective speech analysis techniques. The study was conducted on 20 patients diagnosed with MS according to McDonald’s 2017 criteria and 20 healthy volunteers without any speech or voice pathology. Speech data obtained from patients and healthy individuals were analyzed with the PRAAT speech analysis program, and classification algorithms were tested to determine the most effective classifier in separating specific speech features of MS disease. As a result of the study, the K-nearest neighbor algorithm (K-NN) was found to be the most successful classifier (95%) in distinguishing pathological sounds which were seen in MS patients from those in healthy individuals. The findings obtained in our study can be considered as preliminary data to determine the voice characteristics of MS patients. Full article

The conservation and management of forest areas require knowledge about their extent and attributes on multiple scales. The combination of multiple classifiers has been proposed as an attractive classification approach for improved accuracy and robustness that can efficiently exploit the complementary nature of diverse remote sensing data and the merits of individual classifiers. The aim of this study was to develop and evaluate multiple classifier systems (MCSs) within a cloud-based computing environment for multi-scale forest mapping in Northeastern Greece using passive and active remote sensing data. Five individual machine learning base classifiers were used for class discrimination across the three different hierarchy levels, and five ensemble approaches were used for combining them. In the case of the binary classification scheme in the upper level of the hierarchy for separating woody vegetation (forest and shrubs) from other land, the overall accuracy (OA) slightly increased with the use of the MCS approach, reaching 94%. At the lower hierarchical levels, when using the support vector machine (SVM) base classifier, OA reached 84.13% and 74.89% for forest type and species mapping, respectively, slightly outperforming the MCS approach. Yet, two MCS approaches demonstrated robust performance in terms of per-class accuracy, presenting the highest average F1 score across all classification experiments, indicating balanced misclassification errors across all classes. Since the competence of individual classifiers is dependent on individual scene settings and data characteristics, we suggest that the adoption of MCS systems in efficient computing environments (i.e., cloud) could alleviate the need for algorithm benchmarking for Earth’s surface cover mapping. Full article

It remains challenging to accurately classify complicated clouds owing to the various types of clouds and their distribution on multiple layers. In this paper, multi-band radiation information from the geostationary satellite Himawari-8 and the cloud classification product of the polar orbit satellite CloudSat from June to September 2018 are investigated. Based on sample sets matched by two types of satellite data, a random forest (RF) algorithm was applied to train a model, and a retrieval method was developed for cloud classification. With the use of this method, the sample sets were inverted and classified as clear sky, low clouds, middle clouds, thin cirrus, thick cirrus, multi-layer clouds and deep convection (cumulonimbus) clouds. The results indicate that the average accuracy for all cloud types during the day is 88.4%, and misclassifications mainly occur between low and middle clouds, thick cirrus clouds and cumulonimbus clouds. The average accuracy is 79.1% at night, with more misclassifications occurring between middle clouds, multi-layer clouds and cumulonimbus clouds. Moreover, Typhoon Muifa from 2022 was selected as a sample case, and the cloud type (CLT) product of an FY-4A satellite was used to examine the classification method. In the cloud system of Typhoon Muifa, a cumulonimbus area classified using the method corresponded well with a mesoscale convective system (MCS). Compared to the FY-4A CLT product, the classifications of ice-type (thick cirrus) and multi-layer clouds are effective, and the location, shape and size of these two varieties of cloud are similar. Full article

In order to quickly, nondestructively, and effectively distinguish red jujube varieties, based on the combination of fuzzy theory and improved LDA (iLDA), fuzzy improved linear discriminant analysis (FiLDA) algorithm was proposed to classify near-infrared reflectance (NIR) spectra of red jujube samples. FiLDA shows performs better than iLDA in dealing with NIR spectra containing noise. Firstly, the portable NIR spectrometer was employed to gather the NIR spectra of five kinds of red jujube, and the initial NIR spectra were pretreated by standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (S-G smoothing), mean centering (MC) and Savitzky-Golay filter (S-G filter). Secondly, the high-dimensional spectra were processed for dimension reduction by principal component analysis (PCA). Then, linear discriminant analysis (LDA), iLDA and FiLDA were applied to extract features from the NIR spectra, respectively. Finally, K nearest neighbor (KNN) served as a classifier for the classification of red jujube samples. The highest classification accuracy of this identification system for red jujube, by using FiLDA and KNN, was 94.4%. These results indicated that FiLDA combined with NIR spectroscopy was an available method for identifying the red jujube varieties and this method has wide application prospects. Full article

Water distribution infrastructure (WDI) is well-established and significantly improves living quality. Nonetheless, aging WDI has posed an awkward worldwide problem, wasting natural resources and leading to direct and indirect economic losses. The total losses due to leaks are valued at USD 7 billion per year. In this paper, a multi-classification multi-leak identification (MC-MLI) scheme is developed to combat the captioned problem. In the MC-MLI, a novel adaptive kernel (AK) scheme is developed to adapt to different WDI scenarios. The AK improves the overall identification capability by customizing a weighting vector into the extracted feature vector. Afterwards, a multi-classification (MC) scheme is designed to facilitate efficient adaptation to potentially hostile inhomogeneous WDI scenarios. The MC comprises multiple classifiers for customizing to different pipelines. Each classifier is characterized by the feature vector and corresponding weighting vector and weighting vector pertinent to system requirements, thus rendering the developed scheme strongly adaptive to ever-changing operating environments. Hence, the MC scheme facilitates low-cost, efficient, and accurate water leak detection and provides high practical value to the commercial market. Additionally, graph theory is utilized to model the realistic WDIs, and the experimental results verify that the developed MC-MLI achieves 96% accuracy, 96% sensitivity, and 95% specificity. The average detection time is about 5 s. Full article

Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have identified the potential of remote sensing and multiple source information in impervious surface quantification. Therefore, this study aims to fuse datasets from the Sentinel 1 and 2 Satellites to map the impervious surfaces of nine Pakistani cities and estimate their growth rates from 2016 to 2020 utilizing the random forest algorithm. All bands in the optical and radar images were resampled to 10 m resolution, projected to same coordinate system and geometrically aligned to stack into a single product. The models were then trained, and classifications were validated with land cover samples from Google Earth’s high-resolution images. Overall accuracies of classified maps ranged from 85% to 98% with the resultant quantities showing a strong linear relationship (R-squared value of 0.998) with the Copernicus Global Land Services data. There was up to 9% increase in accuracy and up to 12 % increase in kappa coefficient from the fused data with respect to optical alone. A McNemar test confirmed the superiority of fused data. Finally, the cities had growth rates ranging from 0.5% to 2.5%, with an average of 1.8%. The information obtained can alert urban planners and environmentalists to assess impervious surface impacts in the cities. Full article

The outcome in total knee arthroplasty (TKA) depends on multiples factors, among them is the psychological condition. In addition, up 15 to 30% of the patients that undergo TKA show little or no improvement after surgery, which implies the diagnosis of a painful TKA is a challenge for the orthopedic surgeon, who must rule out a possible metal allergy (MA). It is considered an exclusion diagnosis. Due to the complex relationship between psychological condition and MA, and according to the worse results in patients treated with a hypoallergenic TKA, we asked: (1). What degree of psychological distress (PD) is present in patients who have a hypoallergenic TKA, and how does it influence the results of quality of life (QoL) and functional capacity. (2). Can we develop a new algorithm for patients with a possible MA that improves the outcomes? A pragmatic clinical study was carried out that included patients who underwent hypoallergenic TKA during three consecutive years. Quality of life and functional capacity were measured with (Western Ontario McMaster Universities Osteoarthritis Index) WOMAC index, the Short Form 12 questionnaire (SF-12) questionnaire, and the The EQ-5D-5L questionnaire essentially consists of two pages: the EQ-5D descriptive system and the EQ visual analogue scale (EQ VAS) (Euro-QoL-5D L-VAS (EQ5D)), in all patients. To assess PD, a Psychological Distress Score was developed. SPSS software was performed to statistical analysis, and Student´s test for independent variables with a p < 0.005 as statistically significant. A total of 72 anallergic TKAs in 64 patients were treated during this period; 31.3% of these patients showed features of PD before the surgery. According with the severity of the PD, 60% were classified as severe, 10% as moderate and 30% as mild. Patients with PD had statistically significant worse results on the final WOMAC, SF-12, and EQ5D questionnaires. The final scores of the physical subscale of the SF-12 and EQ5D showed better results in patients diagnosed by psychiatrist. Up to one third of the patients with hypoallergenic TKAs have PD, and their results are clearly inferior to those patients with MA without PD. When PD was diagnosed according with Psychological Distress Score, patients should be carefully assessed in order to determine if a specialist referral is recommended. According with our results, PD should be assessed either by the PCP or by us. If the PD is confirmed, a psychiatry referral is then requested for better preoperative management and treatment. We believe that this approach would lead to better TKA outcomes. Full article

In recent years, interest in scene classification of different indoor-outdoor scene images has increased due to major developments in visual sensor techniques. Scene classification has been demonstrated to be an efficient method for environmental observations but it is a challenging task considering the complexity of multiple objects in scenery images. These images include a combination of different properties and objects i.e., (color, text, and regions) and they are classified on the basis of optimal features. In this paper, an efficient multiclass objects categorization method is proposed for the indoor-outdoor scene classification of scenery images using benchmark datasets. We illustrate two improved methods, fuzzy c-mean and mean shift algorithms, which infer multiple object segmentation in complex images. Multiple object categorization is achieved through multiple kernel learning (MKL), which considers local descriptors and signatures of regions. The relations between multiple objects are then examined by intersection over union algorithm. Finally, scene classification is achieved by using Multi-class Logistic Regression (McLR). Experimental evaluation demonstrated that our scene classification method is superior compared to other conventional methods, especially when dealing with complex images. Our system should be applicable in various domains such as drone targeting, autonomous driving, Global positioning systems, robotics and tourist guide applications. Full article

Breast cancer is one of the major health issues across the world. In this study, a new computer-aided detection (CAD) system is introduced. First, the mammogram images were enhanced to increase the contrast. Second, the pectoral muscle was eliminated and the breast was suppressed from the mammogram. Afterward, some statistical features were extracted. Next, k-nearest neighbor (k-NN) and decision trees classifiers were used to classify the normal and abnormal lesions. Moreover, multiple classifier systems (MCS) was constructed as it usually improves the classification results. The MCS has two structures, cascaded and parallel structures. Finally, two wrapper feature selection (FS) approaches were applied to identify those features, which influence classification accuracy. The two data sets (1) the mammographic image analysis society digital mammogram database (MIAS) and (2) the digital mammography dream challenge were combined together to test the CAD system proposed. The highest accuracy achieved with the proposed CAD system before FS was 99.7% using the Adaboosting of the J48 decision tree classifiers. The highest accuracy after FS was 100%, which was achieved with k-NN classifier. Moreover, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed CAD system was able to accurately classify normal and abnormal lesions in mammogram samples. Full article

While multiple studies show that simulation methods help in educating surgical trainees, few studies have focused on developing systems that help trainees to adopt the most effective body motions. This is the first study to use a Perception Neuron^® system to evaluate the relationship between body motions and simulation scores. Ten medical students participated in this study. All completed two standard tasks with da Vinci Skills Simulator (dVSS) and five standard tasks with thyroidectomy training model. This was repeated. Thyroidectomy training was conducted while participants wore a perception neuron. Motion capture (MC) score that indicated how long the tasks took to complete and each participant’s economy-of-motion that was used was calculated. Correlations between the three scores were assessed by Pearson’s correlation analyses. The 20 trials were categorized as low, moderate, and high overall-proficiency by summing the training model, dVSS, and MC scores. The difference between the low and high overall-proficiency trials in terms of economy-of-motion of the left or right hand was assessed by two-tailed t-test. Relative to cycle 1, the training model, dVSS, and MC scores all increased significantly in cycle 2. Three scores correlated significantly with each other. Six, eight, and six trials were classified as low, moderate, and high overall-proficiency, respectively. Low- and high-scoring trials differed significantly in terms of right (dominant) hand economy-of-motion (675.2 mm and 369.4 mm, respectively) (p = 0.043). Perception Neuron^® system can be applied to simulation-based training of surgical trainees. The motion analysis score is related to the traditional scoring system. Full article

Guangzhou has experienced a rapid urbanization since 1978 when China initiated the economic reform, resulting in significant land use/cover changes (LUC). To produce a time series of accurate LUC dataset that can be used to study urbanization and its impacts, Landsat imagery was used to map LUC changes in Guangzhou from 1987 to 2015 at a three-year interval using a multiple classifier system (MCS). The system was based on a weighted vector to combine base classifiers of different classification algorithms, and was improved using the AdaBoost technique. The new classification method used support vector machines (SVM), C4.5 decision tree, and neural networks (ANN) as the training algorithms of the base classifiers, and produced higher overall classification accuracy (88.12%) and Kappa coefficient (0.87) than each base classifier did. The results of the experiment showed that, based on the accuracy improvement of each class, the overall accuracy was improved effectively, which combined advantages from each base classifier. The new method is of high robustness and low risk of overfitting, and is reliable and accurate, and could be used for analyzing urbanization processes and its impacts. Full article

Landslide scar location is fundamental for the risk management process, e.g., it allows mitigation of these areas, decreasing the associated hazards for the population. Remote sensing data usage is an essential tool for landslide identification, mapping, and monitoring. Despite its potential use for landslide risk management, remote sensing usage does have a few drawbacks. The aforementioned events commonly occur at high steep slope regions, frequently associated with shadow occurrence in satellite images, which impairs the identification process and results in low accuracy classifications. In this sense, this paper aims to evaluate the accuracy of different ensembles of multiple classifier systems (MCSs) for landslide scar identification. A severe landslide event on a steep slope with a high rainfall rate area in the southeast region of Brazil was chosen. Ten supervised classifiers were used to identify this severe event and other possible features for the LANDSAT thematic mapper (TM) from June of 2000. The results were evaluated, and nine MCSs were constructed based on the accuracy of the classifiers. Voting was applied through the ensemble method, coupled with contextual analysis and random selection tie-breaker methods. Accuracy was evaluated for each classification ensemble, and a progressive enhancement in the ensemble accuracy was noted as the least accurate classifiers were removed. The best accuracy for landslide identification emerged from the ensemble of the three most accurate classification results. In summary, MCS application generally improved the classification quality and led to fewer omission errors, coupled with a better classification percentage for the ‘landslide’ class. However, the MCS ensemble algorithm selection must be customized to the purpose of the classification. It is crucial to assess single accuracy indicators of each algorithm to ascertain those with the most consistent performance regarding the final results. Full article

Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+).Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community. Full article