scholarly journals Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery

2020 ◽  
Vol 10 (11) ◽  
pp. 3673 ◽  
Author(s):  
Qingsheng Liu ◽  
Chong Huang ◽  
He Li
Keyword(s):  
Land Cover ◽  
Quality Assessment ◽  
Principal Component ◽  
Support Vector ◽  
Spectral Distortion ◽  
Image Analysis Technique ◽  
Analysis Technique ◽  
Quantitative Analyses ◽  
Land Covers ◽  
Quality Assessments

The existing pansharpening methods applied to recently obtained satellite data can produce spectral distortion. Therefore, quality assessments should be performed to address this. However, quality assessment of the whole image may not be sufficient, because major differences in a given region or land cover can be minimized by small differences in another region or land cover in the image. Thus, it is necessary to evaluate the performance of the pansharpening process for different regions and land covers. In this study, the widely used modified intensity-hue-saturation (mIHS), Gram–Schmidt spectral sharpening (GS), color spectral sharpening (CN), and principal component analysis (PCA) pansharpening methods were applied to Gaofen 2 (GF-2) imagery and evaluated according to region and land-cover type, which was determined via an object-oriented image analysis technique with a support vector machine-supervised method based on several reliable quality indices at the native spatial scale without reference. Both visual and quantitative analyses based on region and land cover indicated that all four approaches satisfied the demands for improving the spatial resolution of the original GF-2 multispectral (MS) image, and mIHS produced results superior to those of the GS, CN, and PC methods by preserving image colors. The results indicated differences in the pansharpening quality among different land covers. Generally, for most land-cover types, the mIHS method better preserved the spectral information and spatial autocorrelation compared with the other methods.

scholarly journals Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices

2018 ◽  
Vol 7 (12) ◽  
pp. 453 ◽  
Author(s):  
Mst Ilme Faridatul ◽  
Bo Wu
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Support Vector ◽  
Landsat 8 ◽  
Spectral Indices ◽  
Urban Land Cover ◽  
Svm Algorithm ◽  
Bare Land ◽  
Land Covers

Urban land cover classification and mapping is an important and ongoing research field in monitoring and managing urban sprawl and terrestrial ecosystems. The changes in land cover largely affect the terrestrial ecosystem, thus information on land cover is important for understanding the ecological environment. Quantification of land cover in urban areas is challenging due to their diversified activities and large spatial and temporal variations. To improve urban land cover classification and mapping, this study presents three new spectral indices and an automated approach to classifying four major urban land types: impervious, bare land, vegetation, and water. A modified normalized difference bare-land index (MNDBI) is proposed to enhance the separation of impervious and bare land. A tasseled cap water and vegetation index (TCWVI) is proposed to enhance the detection of vegetation and water areas. A shadow index (ShDI) is proposed to further improve water detection by separating water from shadows. An approach for optimizing the thresholds of the new indices is also developed. Finally, the optimized thresholds are used to classify land covers using a decision tree algorithm. Using Landsat-8 Operational Land Imager (OLI) data from two study sites (Hong Kong and Dhaka City, Bangladesh) with different urban characteristics, the proposed approach is systematically evaluated. Spectral separability analysis of the new indices is performed and compared with other common indices. The urban land cover classifications achieved by the proposed approach are compared with those of the classic support vector machine (SVM) algorithm. The proposed approach achieves an overall classification accuracy of 94-96%, which is superior to the accuracy of the SVM algorithm.

Automatic Grain Size Determination in Microstructures Using Computer Vision Algorithm Based on Support Vector Machine (SVM)

2017 ◽  
Vol 1143 ◽  
pp. 194-199
Author(s):  
Florin Bogdan Marin ◽  
Florentina Potecaşu ◽  
Mihaela Marin ◽  
Petrică Alexandru
Keyword(s):  
Image Analysis ◽  
Human Error ◽  
Visual Inspection ◽  
Support Vector ◽  
Specific Knowledge ◽  
Image Analysis Technique ◽  
Analysis Technique ◽  
Image Analysis Algorithm ◽  
Important Time ◽  
Vision Algorithm

The properties of materials are identified as the results of its microstructures characteristics. Consequently the task of analysis of microstructure is very important in engineering. There are several methods such as the visual inspection and the semi-automatic inspection by image analysis. Visual inspection by an operator is subject to human error and can take important time. The semiautomatic method using specific algorithm and technique improves performance of the work, however still needs specific knowledge concerning image filters and image analysis technique. This research’s objectives are to present automatic image analysis algorithm to measure grains in microstructure images.

scholarly journals A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

2019 ◽  
Vol 11 (19) ◽  
pp. 2238 ◽  
Author(s):  
Leilei Jiao ◽  
Weiwei Sun ◽  
Gang Yang ◽  
Guangbo Ren ◽  
Yinnian Liu
Keyword(s):  
Land Cover ◽  
Coastal Wetlands ◽  
Hierarchical Classification ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Landsat 8 ◽  
Land Cover Types ◽  
Multispectral Data ◽  
Classification Framework ◽  
Land Covers

Mapping different land cover types with satellite remote sensing data is significant for restoring and protecting natural resources and ecological services in coastal wetlands. In this paper, we propose a hierarchical classification framework (HCF) that implements two levels of classification scheme to identify different land cover types of coastal wetlands. The first level utilizes the designed decision tree to roughly group land covers into four rough classes and the second level combines multiple features (i.e., spectral feature, texture feature and geometric feature) of each class to distinguish different subtypes of land covers in each rough class. Two groups of classification experiments on Landsat and Sentinel multispectral data and China Gaofen (GF)-5 hyperspectral data are carried out in order to testify the classification behaviors of two famous coastal wetlands of China, that is, Yellow River Estuary and Yancheng coastal wetland. Experimental results on Landsat data show that the proposed HCF performs better than support vector machine and random forest in classifying land covers of coastal wetlands. Moreover, HCF is suitable for both multispectral data and hyperspectral data and the GF-5 data is superior to Landsat-8 and Sentinel-2 multispectral data in obtaining fine classification results of coastal wetlands.

scholarly journals Human action recognition using dimensionality reduction and support vector machine

2019 ◽  
pp. 48-53 ◽  
Author(s):  
L V Shiripova ◽  
E V Myasnikov
Keyword(s):  
Support Vector Machine ◽  
Dimensionality Reduction ◽  
Experimental Studies ◽  
Principal Component ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Human Actions ◽  
Analysis Technique

The paper is devoted to the problem of recognizing human actions in videos recorded in the optical range of wavelengths. An approach proposed in this paper consists in the detection of a moving person on a video sequence with the subsequent size normalization, generation of subsequences and dimensionality reduction using the principal component analysis technique. The classification of human actions is carried out using a support vector machine classifier. Experimental studies performed on the Weizmann dataset allowed us to determine the best values of the method parameters. The results showed that with a small number of action classes, high classification accuracy can be achieved.

QSAR Study of PARP Inhibitors by GA-MLR, GA-SVM and GA-ANN Approaches

2020 ◽  
Vol 16 (8) ◽  
pp. 1088-1105
Author(s):  
Nafiseh Vahedi ◽  
Majid Mohammadhosseini ◽  
Mehdi Nekoei
Keyword(s):  
Present Report ◽  
Principal Component ◽  
Parp Inhibitors ◽  
Support Vector ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Test Set ◽  
Non Linear

Background: The poly(ADP-ribose) polymerases (PARP) is a nuclear enzyme superfamily present in eukaryotes. Methods: In the present report, some efficient linear and non-linear methods including multiple linear regression (MLR), support vector machine (SVM) and artificial neural networks (ANN) were successfully used to develop and establish quantitative structure-activity relationship (QSAR) models capable of predicting pEC50 values of tetrahydropyridopyridazinone derivatives as effective PARP inhibitors. Principal component analysis (PCA) was used to a rational division of the whole data set and selection of the training and test sets. A genetic algorithm (GA) variable selection method was employed to select the optimal subset of descriptors that have the most significant contributions to the overall inhibitory activity from the large pool of calculated descriptors. Results: The accuracy and predictability of the proposed models were further confirmed using crossvalidation, validation through an external test set and Y-randomization (chance correlations) approaches. Moreover, an exhaustive statistical comparison was performed on the outputs of the proposed models. The results revealed that non-linear modeling approaches, including SVM and ANN could provide much more prediction capabilities. Conclusion: Among the constructed models and in terms of root mean square error of predictions (RMSEP), cross-validation coefficients (Q2 LOO and Q2 LGO), as well as R2 and F-statistical value for the training set, the predictive power of the GA-SVM approach was better. However, compared with MLR and SVM, the statistical parameters for the test set were more proper using the GA-ANN model.

Application of Machine Learning in Animal Disease Analysis and Prediction

2020 ◽  
Vol 15 ◽  
Author(s):  
Shuwen Zhang ◽  
Qiang Su ◽  
Qin Chen
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Support Vector ◽  
Animal Disease ◽  
Human Beings ◽  
Animal Diseases ◽  
Disease Analysis

Abstract: Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers learn how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and understand its application prospect in animal diseases.

scholarly journals Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽  
2021 ◽  
Vol 26 (13) ◽  
pp. 3983
Author(s):  
Ozren Gamulin ◽  
Marko Škrabić ◽  
Kristina Serec ◽  
Matej Par ◽  
Marija Baković ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Principal Component ◽  
Support Vector ◽  
Gender Recognition ◽  
Proof Of Concept ◽  
Male And Female ◽  
Tooth Type ◽  
Tooth Apex ◽  
The Difference

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

scholarly journals A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1411
Author(s):  
José Luis P. Calle ◽  
Marta Ferreiro-González ◽  
Ana Ruiz-Rodríguez ◽  
Gerardo F. Barbero ◽  
José Á. Álvarez ◽  
...  
Keyword(s):  
Random Forest ◽  
Raw Materials ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Raw Material ◽  
Support Vector ◽  
Protected Designation Of Origin ◽  
Ft Ir

Sherry wine vinegar is a Spanish gourmet product under Protected Designation of Origin (PDO). Before a vinegar can be labeled as Sherry vinegar, the product must meet certain requirements as established by its PDO, which, in this case, means that it has been produced following the traditional solera and criadera ageing system. The quality of the vinegar is determined by many factors such as the raw material, the acetification process or the aging system. For this reason, mainly producers, but also consumers, would benefit from the employment of effective analytical tools that allow precisely determining the origin and quality of vinegar. In the present study, a total of 48 Sherry vinegar samples manufactured from three different starting wines (Palomino Fino, Moscatel, and Pedro Ximénez wine) were analyzed by Fourier-transform infrared (FT-IR) spectroscopy. The spectroscopic data were combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as other nonparametric supervised techniques, namely, support vector machine (SVM) and random forest (RF), for the characterization of the samples. The HCA and PCA results present a clear grouping trend of the vinegar samples according to their raw materials. SVM in combination with leave-one-out cross-validation (LOOCV) successfully classified 100% of the samples, according to the type of wine used for their production. The RF method allowed selecting the most important variables to develop the characteristic fingerprint (“spectralprint”) of the vinegar samples according to their starting wine. Furthermore, the RF model reached 100% accuracy for both LOOCV and out-of-bag (OOB) sets.

scholarly journals Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3003
Author(s):  
Ting Pan ◽  
Haibo Wang ◽  
Haiqing Si ◽  
Yao Li ◽  
Lei Shang
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Practical Significance ◽  
Support Vector ◽  
State Identification ◽  
Feature Parameter ◽  
The Time Domain ◽  
Pilot Fatigue ◽  
Identification Model

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

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